v0.39.0

v0.39.0 Dec. 9, 2021

Enhancements

• Renamed DelayedFeatureTransformer to TimeSeriesFeaturizer and enhanced it to compute rolling features #3028
• Added ability to impute only specific columns in PerColumnImputer #3123
• Added TimeSeriesParametersDataCheck to verify the time series parameters are valid given the number of splits in cross validation #3111

Fixes

• Default parameters for RFRegressorSelectFromModel and RFClassifierSelectFromModel has been fixed to avoid selecting all features #3110

Changes

• Removed reliance on a datetime index for ARIMARegressor and ProphetRegressor #3104
• Included target leakage check when fitting ARIMARegressor to account for the lack of TimeSeriesFeaturizer in ARIMARegressor based pipelines #3104
• Cleaned up and refactored InvalidTargetDataCheck implementation and docstring #3122
• Removed indices information from the output of HighlyNullDataCheck's validate() method #3092
• Added ReplaceNullableTypes component to prepare for handling pandas nullable types. #3090
• Removed unused EnsembleMissingPipelinesError exception definition #3131

Documentation Changes

Testing Changes

• Refactored tests to avoid using importorskip #3126
• Added skip_during_conda test marker to skip tests that are not supposed to run during conda build #3127
• Added skip_if_39 test marker to skip tests that are not supposed to run during python 3.9 #3133

Breaking Changes

• Renamed DelayedFeatureTransformer to TimeSeriesFeaturizer #3028
• ProphetRegressor now requires a datetime column in X represented by the date_index parameter #3104
• Renamed module evalml.data_checks.invalid_target_data_check to evalml.data_checks.invalid_targets_data_check #3122
• Removed unused EnsembleMissingPipelinesError exception definition #3131

v0.38.0

v0.38.0 Nov. 29, 2021

Enhancements

• Added data_check_name attribute to the data check action class #3034
• Added NumWords and NumCharacters primitives to TextFeaturizer and renamed TextFeaturizer` to NaturalLanguageFeaturizer`` #3030
• Added support for scikit-learn > 1.0.0 #3051
• Required the date_index parameter to be specified for time series problems in AutoMLSearch #3041
• Allowed time series pipelines to predict on test datasets whose length is less than or equal to the forecast_horizon. Also allowed the test set index to start at 0. #3071
• Enabled time series pipeline to predict on data with features that are not known-in-advanced #3094

Fixes

• Added in error message when fit and predict/predict_proba data types are different #3036
• Fixed bug where ensembling components could not get converted to JSON format #3049
• Fixed bug where components with tuned integer hyperparameters could not get converted to JSON format #3049
• Included confusion matrix at the pipeline threshold for find_confusion_matrix_per_threshold #3080
• Fixed bug where One Hot Encoder would error out if a non-categorical feature had a missing value #3083
• Fixed bug where features created from categorical columns by Delayed Feature Transformer would be inferred as categorical #3083

Changes

• Delete predict_uses_y estimator attribute #3069
• Change DateTimeFeaturizer to use corresponding Featuretools primitives #3081
• Updated TargetDistributionDataCheck to return metadata details as floats rather strings #3085
• Removed dependency on psutil package #3093

Documentation Changes

• Updated docs to use data check action methods rather than manually cleaning data #3050

Testing Changes

• Updated integration tests to use make_pipeline_from_actions instead of private method #3047

Breaking Changes

• Added data_check_name attribute to the data check action class #3034
• Renamed TextFeaturizer` to NaturalLanguageFeaturizer`` #3030
• Updated the Pipeline.graph_json function to return a dictionary of "from" and "to" edges instead of tuples #3049
• Delete predict_uses_y estimator attribute #3069
• Changed time series problems in AutoMLSearch to need a not-None date_index #3041
• Changed the DelayedFeatureTransformer to throw a ValueError during fit if the date_index is None #3041
• Passing X=None to DelayedFeatureTransformer is deprecated #3041

v0.37.0

v0.37.0 Nov. 10, 2021

Enhancements

• Added find_confusion_matrix_per_threshold to Model Understanding #2972
• Limit computationally-intensive models during AutoMLSearch for certain multiclass problems, allow for opt-in with parameter allow_long_running_models #2982
• Added support for stacked ensemble pipelines to prediction explanations module #2971
• Added integration tests for data checks and data checks actions workflow #2883
• Added a change in pipeline structure to handle categorical columns separately for pipelines in DefaultAlgorithm #2986
• Added an algorithm to DelayedFeatureTransformer to select better lags #3005
• Added AutoML function to access ensemble pipeline's input pipelines IDs #3011

Fixes

• Fixed bug where Oversampler didn't consider boolean columns to be categorical #2980
• Fixed permutation importance failing when target is categorical #3017
• Updated estimator and pipelines' predict, predict_proba, transform, inverse_transform methods to preserve input indices #2979
• Updated demo dataset link for daily min temperatures #3023

Changes

• Updated OutliersDataCheck and UniquenessDataCheck and allow for the suspension of the Nullable types error #3018

Documentation Changes

• Fixed cost benefit matrix demo formatting #2990
• Update ReadMe.md with new badge links and updated installation instructions for conda #2998
• Added more comprehensive doctests #3002

v0.36.0

v0.36.0 Oct. 27, 2021

Enhancements

• Added LIME as an algorithm option for explain_predictions and explain_predictions_best_worst #2905
• Standardized data check messages and added default "rows" and "columns" to data check message details dictionary #2869
• Added rows_of_interest to pipeline utils #2908
• Added support for woodwork version 0.8.2 #2909
• Enhanced the DateTimeFeaturizer to handle NaNs in date features #2909
• Added support for woodwork logical types PostalCode, SubRegionCode, and CountryCode in model understanding tools #2946
• Added Vowpal Wabbit regressor and classifiers #2846

Fixes

• Fixed bug where partial dependence was not respecting the ww schema #2929
• Fixed calculate_permutation_importance for datetimes on StandardScaler #2938
• Fixed SelectColumns to only select available features for feature selection in DefaultAlgorithm #2944
• Fixed DropColumns component not receiving parameters in DefaultAlgorithm #2945
• Fixed bug where trained binary thresholds were not being returned by get_pipeline or clone #2948
• Fixed bug where Oversampler selected ww logical categorical instead of ww semantic category #2946

Changes

• Changed make_pipeline function to place the DateTimeFeaturizer prior to the Imputer so that NaN dates can be imputed #2909
• Refactored OutliersDataCheck and HighlyNullDataCheck to add more descriptive metadata #2907

Documentation Changes

• Added back Future Release section to release notes #2927
• Updated CI to run doctest (docstring tests) and apply necessary fixes to docstrings #2933
• Added documentation for BinaryClassificationPipeline thresholding #2937

Testing Changes

• Fixed dependency checker to catch full names of packages #2930
• Refactored build_conda_pkg to work from a local recipe #2925

Breaking Changes

• Standardized data check messages and added default "rows" and "columns" to data check message details dictionary. This may change the number of messages returned from a data check. #2869

v0.35.0

v0.35.0 Oct. 14, 2021

Enhancements

• Added human-readable pipeline explanations to model understanding #2861
• Updated to support Featuretools 1.0.0 and nlp-primitives 2.0.0 #2848

Fixes

• Fixed bug where long mode for the top level search method was not respected #2875
• Pinned cmdstan to 0.28.0 in cmdstan-builder to prevent future breaking of support for Prophet #2880
• Added Jarque-Bera to the TargetDistributionDataCheck #2891

Changes

• Updated pipelines to use a label encoder component instead of doing encoding on the pipeline level #2821
• Deleted scikit-learn ensembler #2819
• Refactored pipeline building logic out of AutoMLSearch and into IterativeAlgorithm #2854
• Refactored names for methods in ComponentGraph and PipelineBase #2902

Documentation Changes

• Updated install.ipynb to reflect flexibility for cmdstan version installation #2880
• Updated the conda section of our contributing guide #2899

Testing Changes

• Updated test_all_estimators to account for Prophet being allowed for Python 3.9 #2892
• Updated linux tests to use cmdstan-builder==0.0.8 #2880

Breaking Changes

• Updated pipelines to use a label encoder component instead of doing encoding on the pipeline level. This means that pipelines will no longer automatically encode non-numerical targets. Please use a label encoder if working with classification problems and non-numeric targets. #2821
• Deleted scikit-learn ensembler #2819
• IterativeAlgorithm now requires X, y, problem_type as required arguments as well as sampler_name, allowed_model_families, allowed_component_graphs, max_batches, and verbose as optional arguments #2854
• Changed method names of fit_features and compute_final_component_features to fit_and_transform_all_but_final and transform_all_but_final in ComponentGraph, and compute_estimator_features to transform_all_but_final in pipeline classes #2902

v0.34.1rc1

v0.34.1rc1 Oct. 1, 2021

Enhancements

• Updated to support Featuretools 1.0.0 and nlp-primitives 2.0.0 #2848

v0.34.0

v0.34.0 Oct. 1, 2021

Enhancements

• Updated to work with Woodwork 0.8.1 #2783
• Added validation that training_data and training_target are not None in prediction explanations #2787
• Added support for training-only components in pipelines and component graphs #2776
• Added default argument for the parameters value for ComponentGraph.instantiate #2796
• Added TIME_SERIES_REGRESSION to LightGBMRegressor's supported problem types #2793
• Added validation to holdout data passed to predict and predict_proba for time series #2804
• Added information about which row indices are outliers in OutliersDataCheck #2818
• Added verbose flag to top level search() method #2813
• Added support for linting jupyter notebooks and clearing the executed cells and empty cells #2829 #2837
• Added "DROP_ROWS" action to output of OutliersDataCheck.validate() #2820
• Added the ability of AutoMLSearch to accept a SequentialEngine instance as engine input #2838
• Added new label encoder component to EvalML #2853
• Added our own partial dependence implementation #2834

Fixes

• Fixed bug where calculate_permutation_importance was not calculating the right value for pipelines with target transformers #2782
• Fixed bug where transformed target values were not used in fit for time series pipelines #2780
• Fixed bug where score_pipelines method of AutoMLSearch would not work for time series problems #2786
• Removed TargetTransformer class #2833
• Added tests to verify ComponentGraph support by pipelines #2830
• Fixed incorrect parameter for baseline regression pipeline in AutoMLSearch #2847

Changes

• Changed woodwork initialization to use partial schemas #2774
• Made Transformer.transform() an abstract method #2744
• Deleted EmptyDataChecks class #2794
• Removed data check for checking log distributions in make_pipeline #2806
• Changed the minimum woodwork version to 0.8.0 #2783
• Pinned woodwork version to 0.8.0 #2832
• Removed model_family attribute from ComponentBase and transformers #2828
• Limited scikit-learn until new features and errors can be addressed #2842
• Show DeprecationWarning when Sklearn Ensemblers are called #2859

Testing Changes

• Updated matched assertion message regarding monotonic indices in polynomial detrender tests #2811
• Added a test to make sure pip versions match conda versions #2851

Breaking Changes

• Made Transformer.transform() an abstract method #2744
• Deleted EmptyDataChecks class #2794
• Removed data check for checking log distributions in make_pipeline #2806

v0.33.0

v0.33.0 Sep. 15, 2021

Enhancements

Fixes

• Fixed bug where warnings during make_pipeline were not being raised to the user #2765

Changes

• Refactored and removed SamplerBase class #2775

Documentation Changes

• Added docstring linting packages pydocstyle and darglint to make-lint command #2670

v0.32.1

v0.32.1 Sep. 10, 2021

Enhancements

• Added verbose flag to AutoMLSearch to run search in silent mode by default #2645
• Added label encoder to XGBoostClassifier to remove the warning #2701
• Set eval_metric to logloss for XGBoostClassifier #2741
• Added support for woodwork versions 0.7.0 and 0.7.1 #2743
• Changed explain_predictions functions to display original feature values #2759
• Added X_train and y_train to graph_prediction_vs_actual_over_time and get_prediction_vs_actual_over_time_data #2762
• Added forecast_horizon as a required parameter to time series pipelines and AutoMLSearch #2697
• Added predict_in_sample and predict_proba_in_sample methods to time series pipelines to predict on data where the target is known, e.g. cross-validation #2697

Fixes

• Fixed bug where _catch_warnings assumed all warnings were PipelineNotUsed #2753
• Fixed bug where Imputer.transform would erase ww typing information prior to handing data to the SimpleImputer #2752
• Fixed bug where Oversampler could not be copied #2755

Changes

• Deleted drop_nan_target_rows utility method #2737
• Removed default logging setup and debugging log file #2645
• Changed the default n_jobs value for XGBoostClassifier and XGBoostRegressor to 12 #2757
• Changed TimeSeriesBaselineEstimator to only work on a time series pipeline with a DelayedFeaturesTransformer #2697
• Added X_train and y_train as optional parameters to pipeline predict, predict_proba. Only used for time series pipelines #2697
• Added training_data and training_target as optional parameters to explain_predictions and explain_predictions_best_worst to support time series pipelines #2697
• Changed time series pipeline predictions to no longer output series/dataframes padded with NaNs. A prediction will be returned for every row in the X input #2697

Documentation Changes

• Specified installation steps for Prophet #2713
• Added documentation for data exploration on data check actions #2696
• Added a user guide entry for time series modelling #2697

Testing Changes

• Fixed flaky TargetDistributionDataCheck test for very_lognormal distribution #2748

Breaking Changes

• Removed default logging setup and debugging log file #2645
• Added X_train and y_train to graph_prediction_vs_actual_over_time and get_prediction_vs_actual_over_time_data #2762
• Added forecast_horizon as a required parameter to time series pipelines and AutoMLSearch #2697
• Changed TimeSeriesBaselineEstimator to only work on a time series pipeline with a DelayedFeaturesTransformer #2697
• Added X_train and y_train as required parameters for predict and predict_proba in time series pipelines #2697
• Added training_data and training_target as required parameters to explain_predictions and explain_predictions_best_worst for time series pipelines #2697

v0.32.0

v0.32.0 Sep. 1, 2021

Enhancements

• Allow string for engine parameter for AutoMLSearch#2667
• Add ProphetRegressor to AutoML #2619
• Integrated DefaultAlgorithm into AutoMLSearch #2634
• Removed SVM "linear" and "precomputed" kernel hyperparameter options, and improved default parameters #2651
• Updated ComponentGraph initalization to raise ValueError when user attempts to use .y for a component that does not produce a tuple output #2662
• Updated to support Woodwork 0.6.0 #2690
• Updated pipeline graph() to distingush X and y edges #2654
• Added DropRowsTransformer component #2692
• Added DROP_ROWS to _make_component_list_from_actions and clean up metadata #2694

Fixes

• Updated Oversampler logic to select best SMOTE based on component input instead of pipeline input #2695
• Added ability to explicitly close DaskEngine resources to improve runtime and reduce Dask warnings #2667
• Fixed partial dependence bug for ensemble pipelines #2714
• Updated TargetLeakageDataCheck to maintain user-selected logical types #2711

Changes

• Replaced SMOTEOversampler, SMOTENOversampler and SMOTENCOversampler with consolidated Oversampler component #2695
• Removed LinearRegressor from the list of default AutoMLSearch estimators due to poor performance #2660

Documentation Changes

• Updated documentation to make parallelization of AutoML clearer #2667

Testing Changes

• Removes the process-level parallelism from the test_cancel_job test #2666
• Installed numba 0.53 in windows CI to prevent problems installing version 0.54 #2710

Breaking Changes

• Renamed the current top level search method to search_iterative and defined a new search method for the DefaultAlgorithm #2634
• Replaced SMOTEOversampler, SMOTENOversampler and SMOTENCOversampler with consolidated Oversampler component #2695
• Removed LinearRegressor from the list of default AutoMLSearch estimators due to poor performance #2660