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.