Release History

Version 0.1.6 (2021.11)

Enhancement:

All boosting-based methods now support early_termination, which can be used to enable/disable strict early termination for Adaboost training.
Add utility functions imbalanced_ensemble.datasets.generate_imbalance_data() and imbalanced_ensemble.utils.evaluate_print() to ease the test and evaluation.

Bug Fixes:

Fixed Resampling + Bagging models (e.g., OverBagging) raise error when used with base estimators that do not support sample_weight (e.g., sklearn.KNeighborsClassifier).
Fixed AttributeError occurs when initializing bagging-based models.

Enhancement:

imbalanced_ensemble.sampler.under_sampling.RandomUnderSampler now support sample_proba (the probability of each instance being sampled, not sample_weight).

Bug Fixes:

Fixed ValueError when using imbalanced_ensemble.visualizer.ImbalancedEnsembleVisualizer with seaborn v0.11.2.
Fixed all ensemble algorithms (error or performance issue) when the classification targets do not begin with 0.

Enhancement:

imbalanced_ensemble.visualizer.ImbalancedEnsembleVisualizer.performance_lineplot(): add option on_metrics to select evaluation metrics to include in the plot.
imbalanced_ensemble.visualizer.ImbalancedEnsembleVisualizer.confusion_matrix_heatmap(): add option false_pred_only to control whether to plot only the false predictions in the confusion matrix.
Add some utilities for data visualization in imbalanced_ensemble.utils._plot.

Documentation:

Maintenance:

imbalanced_ensemble.utils.testing.all_estimators() now support 'ensemble' type_filter.
Renamed some functions in imbalanced_ensemble.utils._validation_param to improve readability

Bug Fixes:

Bug Fixes:

Fixed a typo bug in imbalanced_ensemble.ensemble.BalanceCascadeClassifier.
Fixed an import Error in imbalanced_ensemble.ensembleCompatibleAdaBoostClassifier.

Enhancement:

Add support for metric functions that take probability as input.
Boosting-based classifiers now will print a message when the training is early terminated.
imbalanced_ensemble.visualizer.ImbalancedEnsembleVisualizer.performance_lineplot(): granularity now can be automatically set.

Maintenance:

All ensemble classifiers now can be directly imported from the imbalanced_ensemble.ensemble module.
The default value of train_verbose of Classifier.fit(): True -> False.
The default value of n_estimators of Classifier.__init__(): 50 for all ensemble classifiers.
The default value of granularity of Visualizer.fit(): 5 -> None (automatically determined).
imbalanced_ensemble.visualizer.ImbalancedEnsembleVisualizer.confusion_matrix_heatmap(): swap rows and columns, now rows/columns correspond to datasets/methods.

Bug Fixes:

Bug Fixes:

Initial release.