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@angela97lin angela97lin released this 03 Aug 18:58
· 1610 commits to main since this release
v0.12.0
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Learn about vigilant mode.

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
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