Release History

Version 0.2.0 (2023.02)

Enhancement:

• Enable CircleCI with CodeCov report.

• Easier usage:

  • the package is now imported as imbens

  • all samplers can be directly accessed in imbens.sampler

Maintenance:

• Complement unit tests (59% -> 96% coverage).

• Set default k_neighbors=1 for SMOTEBagging to prevent error in few-shot cases.

• Set default cluster_balance_threshold=0.1 for KmeansSMOTEBoost to prevent error in few-shot cases.

• Add decision_function() for supported ensemble classifiers.

• The parameter base_sampler is renamed to sampler.

• The attribute base_sampler_ is renamed to sampler_.

• Bump supported Python version to 3.8, 3.9, 3.10, 3.11.

• Following sklearn version >1.2, for all ensemble classifiers,

  • the parameter base_estimator is renamed to estimator.

  • the attribute base_estimator_ is renamed to estimator_.

Bug Fixes:

• Add missing comma in the INSTALL_REQUIRES list which breaks conda env export.

• Fix BalanceCascade and SelfPacedEnsemble’s _make_sampler() behaviour.

• Fix BalanceCascade and SelfPacedEnsemble parameter check.

• Fix cost_matrix type check for cost-sensitive methods

• Fix SVMSMOTE with sample_weight

• Fix CompatibleAdaBoost with train_verbose

• Fix samplers in/output type consistency

Version 0.1.7 (2022.01)

Enhancement:

• Add feature_importances_ attribute for supported methods:

  • imbens.ensemble.AdaCostClassifier

  • imbens.ensemble.AdaUBoostClassifier

  • imbens.ensemble.AsymBoostClassifier

  • imbens.ensemble.BalanceCascadeClassifier

  • imbens.ensemble.BalancedRandomForestClassifier

  • imbens.ensemble.CompatibleAdaBoostClassifier

  • imbens.ensemble.KmeansSMOTEBoostClassifier

  • imbens.ensemble.OverBoostClassifier

  • imbens.ensemble.RUSBoostClassifier

  • imbens.ensemble.SMOTEBoostClassifier

  • imbens.ensemble.SelfPacedEnsembleClassifier

Documentation:

• Paper describing this package “IMBENS: Ensemble Class-imbalanced Learning in Python”.

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 imbens.datasets.generate_imbalance_data() and imbens.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.

Version 0.1.5 (2021.08)

Enhancement:

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

Bug Fixes:

• Fixed ValueError when using imbens.visualizer.ImbalancedEnsembleVisualizer with seaborn v0.11.2.

• Fixed all ensemble algorithms (error or performance issue) when the classification targets do not begin with 0.

Version 0.1.4 (2021.06)

Enhancement:

• imbens.visualizer.ImbalancedEnsembleVisualizer.performance_lineplot(): add option on_metrics to select evaluation metrics to include in the plot.

• imbens.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 imbens.utils._plot.

Documentation:

• Add more comprehensive examples in the examples gallery (11 new, 16 in total).

• Add a Chinese README.

Maintenance:

• imbens.utils.testing.all_estimators() now support 'ensemble' type_filter.

• Renamed some functions in imbens.utils._validation_param to improve readability

Bug Fixes:

• Fixed typo bugs in:

  • imbens.ensemble.KmeansSMOTEBoostClassifier

  • imbens.ensemble.SMOTEBoostClassifier

  • imbens.ensemble.SMOTEBaggingClassifier

Version 0.1.3 (2021.06)

Bug Fixes:

• Fixed a typo bug in imbens.ensemble.BalanceCascadeClassifier.

• Fixed an import Error in imbens.ensembleCompatibleAdaBoostClassifier.

Version 0.1.2 (2021.05)

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.

• imbens.visualizer.ImbalancedEnsembleVisualizer.performance_lineplot(): granularity now can be automatically set.

Maintenance:

• All ensemble classifiers now can be directly imported from the imbens.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).

• imbens.visualizer.ImbalancedEnsembleVisualizer.confusion_matrix_heatmap(): swap rows and columns, now rows/columns correspond to datasets/methods.

Bug Fixes:

• Fixed ZeroDivisionError when using imbens.sampler.SelfPacedUnderSampler.

Version 0.1.1 (2021.05)

Bug Fixes:

• Unexpected print messages when using the imbens.pipeline module.

Version 0.1.0 (2021.05)

Initial release.