Release history

Version 0.11.0 (Under development)

Changelog

Version 0.10.1

December 28, 2022

Changelog

Bug fixes

• Fix a regression in over-sampler where the string minority was rejected as an unvalid sampling strategy. #964 by Prakhyath Bhandary.

Version 0.10.0

December 9, 2022

Changelog

Bug fixes

• Make sure that Substitution is working with python -OO that replace __doc__ by None. #953 bu Guillaume Lemaitre.

Compatibility

• Maintenance release for be compatible with scikit-learn >= 1.0.2. #946, #947, #949 by Guillaume Lemaitre.

• Add support for automatic parameters validation as in scikit-learn >= 1.2. #955 by Guillaume Lemaitre.

• Add support for feature_names_in_ as well as get_feature_names_out for all samplers. #959 by Guillaume Lemaitre.

Deprecation

• The parameter n_jobs has been deprecated from the classes ADASYN, BorderlineSMOTE, SMOTE, SMOTENC, SMOTEN, and SVMSMOTE. Instead, pass a nearest neighbors estimator where n_jobs is set. #887 by Guillaume Lemaitre.

• The parameter base_estimator is deprecated and will be removed in version 0.12. It is impacted the following classes: BalancedBaggingClassifier, EasyEnsembleClassifier, RUSBoostClassifier. #946 by Guillaume Lemaitre.

Enhancements

• Add support to accept compatible NearestNeighbors objects by only duck-typing. For instance, it allows to accept cuML instances. #858 by NV-jpt and Guillaume Lemaitre.

Version 0.9.1

May 16, 2022

Changelog

This release provides fixes that make imbalanced-learn works with the latest release (1.1.0) of scikit-learn.

Version 0.9.0

January 11, 2022

Changelog

This release is mainly providing fixes that make imbalanced-learn works with the latest release (1.0.2) of scikit-learn.

Version 0.8.1

September 29, 2020

Changelog

Maintenance

• Make imbalanced-learn compatible with scikit-learn 1.0. #864 by Guillaume Lemaitre.

Version 0.8.0

February 18, 2021

Changelog

New features

• Add the the function imblearn.metrics.macro_averaged_mean_absolute_error returning the average across class of the MAE. This metric is used in ordinal classification. #780 by Aurélien Massiot.

• Add the class imblearn.metrics.pairwise.ValueDifferenceMetric to compute pairwise distances between samples containing only categorical values. #796 by Guillaume Lemaitre.

• Add the class imblearn.over_sampling.SMOTEN to over-sample data only containing categorical features. #802 by Guillaume Lemaitre.

• Add the possibility to pass any type of samplers in imblearn.ensemble.BalancedBaggingClassifier unlocking the implementation of methods based on resampled bagging. #808 by Guillaume Lemaitre.

Enhancements

• Add option output_dict in imblearn.metrics.classification_report_imbalanced to return a dictionary instead of a string. #770 by Guillaume Lemaitre.

• Added an option to generate smoothed bootstrap in imblearn.over_sampling.RandomOverSampler. It is controls by the parameter shrinkage. This method is also known as Random Over-Sampling Examples (ROSE). #754 by Andrea Lorenzon and Guillaume Lemaitre.

Bug fixes

• Fix a bug in imblearn.under_sampling.ClusterCentroids where voting="hard" could have lead to select a sample from any class instead of the targeted class. #769 by Guillaume Lemaitre.

• Fix a bug in imblearn.FunctionSampler where validation was performed even with validate=False when calling fit. #790 by Guillaume Lemaitre.

Maintenance

• Remove requirements files in favour of adding the packages in the extras_require within the setup.py file. #816 by Guillaume Lemaitre.

• Change the website template to use pydata-sphinx-theme. #801 by Guillaume Lemaitre.

Deprecation

• The context manager imblearn.utils.testing.warns is deprecated in 0.8 and will be removed 1.0. #815 by Guillaume Lemaitre.

Version 0.7.0

June 9, 2020

Changelog

Maintenance

• Ensure that imblearn.pipeline.Pipeline is working when memory is activated and joblib==0.11. #687 by Christos Aridas.

• Refactor common test to use the dev tools from scikit-learn 0.23. #710 by Guillaume Lemaitre.

• Remove FutureWarning issued by scikit-learn 0.23. #710 by Guillaume Lemaitre.

• Impose keywords only argument as in scikit-learn. #721 by Guillaume Lemaitre.

Changed models

The following models might give some different results due to changes:

• imblearn.ensemble.BalancedRandomForestClassifier

Bug fixes

• Change the default value min_samples_leaf to be consistent with scikit-learn. #711 by zerolfx.

• Fix a bug due to change in scikit-learn 0.23 in imblearn.metrics.make_index_balanced_accuracy. The function was unusable. #710 by Guillaume Lemaitre.

• Raise a proper error message when only numerical or categorical features are given in imblearn.over_sampling.SMOTENC. #720 by Guillaume Lemaitre.

• Fix a bug when the median of the standard deviation is null in imblearn.over_sampling.SMOTENC. #675 by bganglia.

Enhancements

• The classifier implemented in imbalanced-learn, imblearn.ensemble.BalancedBaggingClassifier, imblearn.ensemble.BalancedRandomForestClassifier, imblearn.ensemble.EasyEnsembleClassifier, and imblearn.ensemble.RUSBoostClassifier, accept sampling_strategy with the same key than in y without the need of encoding y in advance. #718 by Guillaume Lemaitre.

• Lazy import keras module when importing imblearn.keras #719 by Guillaume Lemaitre.

Deprecation

• Deprecation of the parameters n_jobs in imblearn.under_sampling.ClusterCentroids since it was used by sklearn.cluster.KMeans which deprecated it. #710 by Guillaume Lemaitre.

• Deprecation of passing keyword argument by position similarly to scikit-learn. #721 by Guillaume lemaitre.

Version 0.6.2

February 16, 2020

This is a bug-fix release to resolve some issues regarding the handling the input and the output format of the arrays.

Changelog

• Allow column vectors to be passed as targets. #673 by Christos Aridas.

• Better input/output handling for pandas, numpy and plain lists. #681 by Christos Aridas.

Version 0.6.1

December 7, 2019

This is a bug-fix release to primarily resolve some packaging issues in version 0.6.0. It also includes minor documentation improvements and some bug fixes.

Changelog

Bug fixes

• Fix a bug in imblearn.ensemble.BalancedRandomForestClassifier leading to a wrong number of samples used during fitting due max_samples and therefore a bad computation of the OOB score. #656 by Guillaume Lemaitre.

Version 0.6.0

December 5, 2019

Changelog

Changed models

The following models might give some different sampling due to changes in scikit-learn:

• imblearn.under_sampling.ClusterCentroids

• imblearn.under_sampling.InstanceHardnessThreshold

The following samplers will give different results due to change linked to the random state internal usage:

• imblearn.over_sampling.ADASYN

• imblearn.over_sampling.SMOTENC

Bug fixes

• imblearn.under_sampling.InstanceHardnessThreshold now take into account the random_state and will give deterministic results. In addition, cross_val_predict is used to take advantage of the parallelism. #599 by Shihab Shahriar Khan.

• Fix a bug in imblearn.ensemble.BalancedRandomForestClassifier leading to a wrong computation of the OOB score. #656 by Guillaume Lemaitre.

Maintenance

• Update imports from scikit-learn after that some modules have been privatize. The following import have been changed: sklearn.ensemble._base._set_random_states, sklearn.ensemble._forest._parallel_build_trees, sklearn.metrics._classification._check_targets, sklearn.metrics._classification._prf_divide, sklearn.utils.Bunch, sklearn.utils._safe_indexing, sklearn.utils._testing.assert_allclose, sklearn.utils._testing.assert_array_equal, sklearn.utils._testing.SkipTest. #617 by Guillaume Lemaitre.

• Synchronize imblearn.pipeline with sklearn.pipeline. #620 by Guillaume Lemaitre.

• Synchronize imblearn.ensemble.BalancedRandomForestClassifier and add parameters max_samples and ccp_alpha. #621 by Guillaume Lemaitre.

Enhancement

• imblearn.under_sampling.RandomUnderSampling, imblearn.over_sampling.RandomOverSampling, imblearn.datasets.make_imbalance accepts Pandas DataFrame in and will output Pandas DataFrame. Similarly, it will accepts Pandas Series in and will output Pandas Series. #636 by Guillaume Lemaitre.

• imblearn.FunctionSampler accepts a parameter validate allowing to check or not the input X and y. #637 by Guillaume Lemaitre.

• imblearn.under_sampling.RandomUnderSampler, imblearn.over_sampling.RandomOverSampler can resample when non finite values are present in X. #643 by Guillaume Lemaitre.

• All samplers will output a Pandas DataFrame if a Pandas DataFrame was given as an input. #644 by Guillaume Lemaitre.

• The samples generation in imblearn.over_sampling.ADASYN, imblearn.over_sampling.SMOTE, imblearn.over_sampling.BorderlineSMOTE, imblearn.over_sampling.SVMSMOTE, imblearn.over_sampling.KMeansSMOTE, imblearn.over_sampling.SMOTENC is now vectorize with giving an additional speed-up when X in sparse. #596 and #649 by Matt Eding.

Deprecation

• The following classes have been removed after 2 deprecation cycles: ensemble.BalanceCascade and ensemble.EasyEnsemble. #617 by Guillaume Lemaitre.

• The following functions have been removed after 2 deprecation cycles: utils.check_ratio. #617 by Guillaume Lemaitre.

• The parameter ratio and return_indices has been removed from all samplers. #617 by Guillaume Lemaitre.

• The parameters m_neighbors, out_step, kind, svm_estimator have been removed from the imblearn.over_sampling.SMOTE. #617 by Guillaume Lemaitre.

Version 0.5.0

June 28, 2019

Changelog

Changed models

The following models or function might give different results even if the same data X and y are the same.

• imblearn.ensemble.RUSBoostClassifier default estimator changed from sklearn.tree.DecisionTreeClassifier with full depth to a decision stump (i.e., tree with max_depth=1).

Documentation

• Correct the definition of the ratio when using a float in sampling strategy for the over-sampling and under-sampling. #525 by Ariel Rossanigo.

• Add imblearn.over_sampling.BorderlineSMOTE and imblearn.over_sampling.SVMSMOTE in the API documenation. #530 by Guillaume Lemaitre.

Enhancement

• Add Parallelisation for SMOTEENN and SMOTETomek. #547 by Michael Hsieh.

• Add imblearn.utils._show_versions. Updated the contribution guide and issue template showing how to print system and dependency information from the command line. #557 by Alexander L. Hayes.

• Add imblearn.over_sampling.KMeansSMOTE which is an over-sampler clustering points before to apply SMOTE. #435 by Stephan Heijl.

Maintenance

• Make it possible to import imblearn and access submodule. #500 by Guillaume Lemaitre.

• Remove support for Python 2, remove deprecation warning from scikit-learn 0.21. #576 by Guillaume Lemaitre.

Bug

• Fix wrong usage of keras.layers.BatchNormalization in porto_seguro_keras_under_sampling.py example. The batch normalization was moved before the activation function and the bias was removed from the dense layer. #531 by Guillaume Lemaitre.

• Fix bug which converting to COO format sparse when stacking the matrices in imblearn.over_sampling.SMOTENC. This bug was only old scipy version. #539 by Guillaume Lemaitre.

• Fix bug in imblearn.pipeline.Pipeline where None could be the final estimator. #554 by Oliver Rausch.

• Fix bug in imblearn.over_sampling.SVMSMOTE and imblearn.over_sampling.BorderlineSMOTE where the default parameter of n_neighbors was not set properly. #578 by Guillaume Lemaitre.

• Fix bug by changing the default depth in imblearn.ensemble.RUSBoostClassifier to get a decision stump as a weak learner as in the original paper. #545 by Christos Aridas.

• Allow to import keras directly from tensorflow in the imblearn.keras. #531 by Guillaume Lemaitre.

Version 0.4.2

October 21, 2018

Changelog

Bug fixes

• Fix a bug in imblearn.over_sampling.SMOTENC in which the the median of the standard deviation instead of half of the median of the standard deviation. By Guillaume Lemaitre in #491.

• Raise an error when passing target which is not supported, i.e. regression target or multilabel targets. Imbalanced-learn does not support this case. By Guillaume Lemaitre in #490.

• Fix a bug in imblearn.over_sampling.SMOTENC in which a sparse matrices were densify during inverse_transform. By Guillaume Lemaitre in #495.

• Fix a bug in imblearn.over_sampling.SMOTE_NC in which a the tie breaking was wrongly sampling. By Guillaume Lemaitre in #497.

Version 0.4

October 12, 2018

Warning

Version 0.4 is the last version of imbalanced-learn to support Python 2.7 and Python 3.4. Imbalanced-learn 0.5 will require Python 3.5 or higher.

Highlights

This release brings its set of new feature as well as some API changes to strengthen the foundation of imbalanced-learn.

As new feature, 2 new modules imblearn.keras and imblearn.tensorflow have been added in which imbalanced-learn samplers can be used to generate balanced mini-batches.

The module imblearn.ensemble has been consolidated with new classifier: imblearn.ensemble.BalancedRandomForestClassifier, imblearn.ensemble.EasyEnsembleClassifier, imblearn.ensemble.RUSBoostClassifier.

Support for string has been added in imblearn.over_sampling.RandomOverSampler and imblearn.under_sampling.RandomUnderSampler. In addition, a new class imblearn.over_sampling.SMOTENC allows to generate sample with data sets containing both continuous and categorical features.

The imblearn.over_sampling.SMOTE has been simplified and break down to 2 additional classes: imblearn.over_sampling.SVMSMOTE and imblearn.over_sampling.BorderlineSMOTE.

There is also some changes regarding the API: the parameter sampling_strategy has been introduced to replace the ratio parameter. In addition, the return_indices argument has been deprecated and all samplers will exposed a sample_indices_ whenever this is possible.

Changelog

API

• Replace the parameter ratio by sampling_strategy. #411 by Guillaume Lemaitre.

• Enable to use a float with binary classification for sampling_strategy. #411 by Guillaume Lemaitre.

• Enable to use a list for the cleaning methods to specify the class to sample. #411 by Guillaume Lemaitre.

• Replace fit_sample by fit_resample. An alias is still available for backward compatibility. In addition, sample has been removed to avoid resampling on different set of data. #462 by Guillaume Lemaitre.

New features

• Add a keras and tensorflow modules to create balanced mini-batches generator. #409 by Guillaume Lemaitre.

• Add imblearn.ensemble.EasyEnsembleClassifier which create a bag of AdaBoost classifier trained on balanced bootstrap samples. #455 by Guillaume Lemaitre.

• Add imblearn.ensemble.BalancedRandomForestClassifier which balanced each bootstrap provided to each tree of the forest. #459 by Guillaume Lemaitre.

• Add imblearn.ensemble.RUSBoostClassifier which applied a random under-sampling stage before each boosting iteration of AdaBoost. #469 by Guillaume Lemaitre.

• Add imblern.over_sampling.SMOTENC which generate synthetic samples on data set with heterogeneous data type (continuous and categorical features). #412 by Denis Dudnik and Guillaume Lemaitre.

Enhancement

• Add a documentation node to create a balanced random forest from a balanced bagging classifier. #372 by Guillaume Lemaitre.

• Document the metrics to evaluate models on imbalanced dataset. #367 by Guillaume Lemaitre.

• Add support for one-vs-all encoded target to support keras. #409 by Guillaume Lemaitre.

• Adding specific class for borderline and SVM SMOTE using BorderlineSMOTE and SVMSMOTE. #440 by Guillaume Lemaitre.

• Allow imblearn.over_sampling.RandomOverSampler can return indices using the attributes return_indices. #439 by Hugo Gascon and Guillaume Lemaitre.

• Allow imblearn.under_sampling.RandomUnderSampler and imblearn.over_sampling.RandomOverSampler to sample object array containing strings. #451 by Guillaume Lemaitre.

Bug fixes

• Fix bug in metrics.classification_report_imbalanced for which y_pred and y_true where inversed. #394 by @Ole Silvig <klizter>.

• Fix bug in ADASYN to consider only samples from the current class when generating new samples. #354 by Guillaume Lemaitre.

• Fix bug which allow for sorted behavior of sampling_strategy dictionary and thus to obtain a deterministic results when using the same random state. #447 by Guillaume Lemaitre.

• Force to clone scikit-learn estimator passed as attributes to samplers. #446 by Guillaume Lemaitre.

• Fix bug which was not preserving the dtype of X and y when generating samples. #450 by Guillaume Lemaitre.

• Add the option to pass a Memory object to make_pipeline like in pipeline.Pipeline class. #458 by Christos Aridas.

Maintenance

• Remove deprecated parameters in 0.2 - #331 by Guillaume Lemaitre.

• Make some modules private. #452 by Guillaume Lemaitre.

• Upgrade requirements to scikit-learn 0.20. #379 by Guillaume Lemaitre.

• Catch deprecation warning in testing. #441 by Guillaume Lemaitre.

• Refactor and impose pytest style tests. #470 by Guillaume Lemaitre.

Documentation

• Remove some docstring which are not necessary. #454 by Guillaume Lemaitre.

• Fix the documentation of the sampling_strategy parameters when used as a float. #480 by Guillaume Lemaitre.

Deprecation

• Deprecate ratio in favor of sampling_strategy. #411 by Guillaume Lemaitre.

• Deprecate the use of a dict for cleaning methods. a list should be used. #411 by Guillaume Lemaitre.

• Deprecate random_state in imblearn.under_sampling.NearMiss, imblearn.under_sampling.EditedNearestNeighbors, imblearn.under_sampling.RepeatedEditedNearestNeighbors, imblearn.under_sampling.AllKNN, imblearn.under_sampling.NeighbourhoodCleaningRule, imblearn.under_sampling.InstanceHardnessThreshold, imblearn.under_sampling.CondensedNearestNeighbours.

• Deprecate kind, out_step, svm_estimator, m_neighbors in imblearn.over_sampling.SMOTE. User should use imblearn.over_sampling.SVMSMOTE and imblearn.over_sampling.BorderlineSMOTE. #440 by Guillaume Lemaitre.

• Deprecate imblearn.ensemble.EasyEnsemble in favor of meta-estimator imblearn.ensemble.EasyEnsembleClassifier which follow the exact algorithm described in the literature. #455 by Guillaume Lemaitre.

• Deprecate imblearn.ensemble.BalanceCascade. #472 by Guillaume Lemaitre.

• Deprecate return_indices in all samplers. Instead, an attribute sample_indices_ is created whenever the sampler is selecting a subset of the original samples. #474 by @Guillaume Lemaitre <glemaitre.

Version 0.3

February 22, 2018

Changelog

• Pytest is used instead of nosetests. #321 by Joan Massich.

• Added a User Guide and extended some examples. #295 by Guillaume Lemaitre.

• Fixed a bug in utils.check_ratio such that an error is raised when the number of samples required is negative. #312 by Guillaume Lemaitre.

• Fixed a bug in under_sampling.NearMiss version 3. The indices returned were wrong. #312 by Guillaume Lemaitre.

• Fixed bug for ensemble.BalanceCascade and combine.SMOTEENN and SMOTETomek. #295 by Guillaume Lemaitre.

• Fixed bug for check_ratio to be able to pass arguments when ratio is a callable. #307 by Guillaume Lemaitre.

• Turn off steps in pipeline.Pipeline using the None object. By Christos Aridas.

• Add a fetching function datasets.fetch_datasets in order to get some imbalanced datasets useful for benchmarking. #249 by Guillaume Lemaitre.

• All samplers accepts sparse matrices with defaulting on CSR type. #316 by Guillaume Lemaitre.

• datasets.make_imbalance take a ratio similarly to other samplers. It supports multiclass. #312 by Guillaume Lemaitre.

• All the unit tests have been factorized and a utils.check_estimators has been derived from scikit-learn. By Guillaume Lemaitre.

• Script for automatic build of conda packages and uploading. #242 by Guillaume Lemaitre

• Remove seaborn dependence and improve the examples. #264 by Guillaume Lemaitre.

• adapt all classes to multi-class resampling. #290 by Guillaume Lemaitre

• __init__ has been removed from the base.SamplerMixin to create a real mixin class. #242 by Guillaume Lemaitre.

• creation of a module exceptions to handle consistant raising of errors. #242 by Guillaume Lemaitre.

• creation of a module utils.validation to make checking of recurrent patterns. #242 by Guillaume Lemaitre.

• move the under-sampling methods in prototype_selection and prototype_generation submodule to make a clearer dinstinction. #277 by Guillaume Lemaitre.

• change ratio such that it can adapt to multiple class problems. #290 by Guillaume Lemaitre.

• Deprecation of the use of min_c_ in datasets.make_imbalance. #312 by Guillaume Lemaitre

• Deprecation of the use of float in datasets.make_imbalance for the ratio parameter. #290 by Guillaume Lemaitre.

• deprecate the use of float as ratio in favor of dictionary, string, or callable. #290 by Guillaume Lemaitre.

Version 0.2

January 1, 2017

Changelog

• Fixed a bug in under_sampling.NearMiss which was not picking the right samples during under sampling for the method 3. By Guillaume Lemaitre.

• Fixed a bug in ensemble.EasyEnsemble, correction of the random_state generation. By Guillaume Lemaitre and Christos Aridas.

• Fixed a bug in under_sampling.RepeatedEditedNearestNeighbours, add additional stopping criterion to avoid that the minority class become a majority class or that a class disappear. By Guillaume Lemaitre.

• Fixed a bug in under_sampling.AllKNN, add stopping criteria to avoid that the minority class become a majority class or that a class disappear. By Guillaume Lemaitre.

• Fixed a bug in under_sampling.CondensedNeareastNeigbour, correction of the list of indices returned. By Guillaume Lemaitre.

• Fixed a bug in ensemble.BalanceCascade, solve the issue to obtain a single array if desired. By Guillaume Lemaitre.

• Fixed a bug in pipeline.Pipeline, solve to embed Pipeline in other Pipeline. #231 by Christos Aridas.

• Fixed a bug in pipeline.Pipeline, solve the issue to put to sampler in the same Pipeline. #188 by Christos Aridas.

• Fixed a bug in under_sampling.CondensedNeareastNeigbour, correction of the shape of sel_x when only one sample is selected. By Aliaksei Halachkin.

• Fixed a bug in under_sampling.NeighbourhoodCleaningRule, selecting neighbours instead of minority class misclassified samples. #230 by Aleksandr Loskutov.

• Fixed a bug in over_sampling.ADASYN, correction of the creation of a new sample so that the new sample lies between the minority sample and the nearest neighbour. #235 by Rafael Wampfler.

• Added AllKNN under sampling technique. By Dayvid Oliveira.

• Added a module metrics implementing some specific scoring function for the problem of balancing. #204 by Guillaume Lemaitre and Christos Aridas.

• Added support for bumpversion. By Guillaume Lemaitre.

• Validate the type of target in binary samplers. A warning is raised for the moment. By Guillaume Lemaitre and Christos Aridas.

• Change from cross_validation module to model_selection module for sklearn deprecation cycle. By Dayvid Oliveira and Christos Aridas.

• size_ngh has been deprecated in combine.SMOTEENN. Use n_neighbors instead. By Guillaume Lemaitre, Christos Aridas, and Dayvid Oliveira.

• size_ngh has been deprecated in under_sampling.EditedNearestNeighbors. Use n_neighbors instead. By Guillaume Lemaitre, Christos Aridas, and Dayvid Oliveira.

• size_ngh has been deprecated in under_sampling.CondensedNeareastNeigbour. Use n_neighbors instead. By Guillaume Lemaitre, Christos Aridas, and Dayvid Oliveira.

• size_ngh has been deprecated in under_sampling.OneSidedSelection. Use n_neighbors instead. By Guillaume Lemaitre, Christos Aridas, and Dayvid Oliveira.

• size_ngh has been deprecated in under_sampling.NeighbourhoodCleaningRule. Use n_neighbors instead. By Guillaume Lemaitre, Christos Aridas, and Dayvid Oliveira.

• size_ngh has been deprecated in under_sampling.RepeatedEditedNearestNeighbours. Use n_neighbors instead. By Guillaume Lemaitre, Christos Aridas, and Dayvid Oliveira.

• size_ngh has been deprecated in under_sampling.AllKNN. Use n_neighbors instead. By Guillaume Lemaitre, Christos Aridas, and Dayvid Oliveira.

• Two base classes BaseBinaryclassSampler and BaseMulticlassSampler have been created to handle the target type and raise warning in case of abnormality. By Guillaume Lemaitre and Christos Aridas.

• Move random_state to be assigned in the SamplerMixin initialization. By Guillaume Lemaitre.

• Provide estimators instead of parameters in combine.SMOTEENN and combine.SMOTETomek. Therefore, the list of parameters have been deprecated. By Guillaume Lemaitre and Christos Aridas.

• k has been deprecated in over_sampling.ADASYN. Use n_neighbors instead. #183 by Guillaume Lemaitre.

• k and m have been deprecated in over_sampling.SMOTE. Use k_neighbors and m_neighbors instead. #182 by Guillaume Lemaitre.

• n_neighbors accept KNeighborsMixin based object for under_sampling.EditedNearestNeighbors, under_sampling.CondensedNeareastNeigbour, under_sampling.NeighbourhoodCleaningRule, under_sampling.RepeatedEditedNearestNeighbours, and under_sampling.AllKNN. #109 by Guillaume Lemaitre.

• Replace some remaining UnbalancedDataset occurences. By Francois Magimel.

• Added doctest in the documentation. By Guillaume Lemaitre.

Version 0.1

December 26, 2016

Changelog

• First release of the stable API. By :user;`Fernando Nogueira <fmfn>`, Guillaume Lemaitre, Christos Aridas, and Dayvid Oliveira.

• Under-sampling

  1. Random majority under-sampling with replacement

  2. Extraction of majority-minority Tomek links

  3. Under-sampling with Cluster Centroids

  4. NearMiss-(1 & 2 & 3)

  5. Condensend Nearest Neighbour

  6. One-Sided Selection

  7. Neighboorhood Cleaning Rule

  8. Edited Nearest Neighbours

  9. Instance Hardness Threshold

  10. Repeated Edited Nearest Neighbours

• Over-sampling

  1. Random minority over-sampling with replacement

  2. SMOTE - Synthetic Minority Over-sampling Technique

  3. bSMOTE(1 & 2) - Borderline SMOTE of types 1 and 2

  4. SVM SMOTE - Support Vectors SMOTE

  5. ADASYN - Adaptive synthetic sampling approach for imbalanced learning

• Over-sampling followed by under-sampling

  1. SMOTE + Tomek links

  2. SMOTE + ENN

• Ensemble sampling

  1. EasyEnsemble

  2. BalanceCascade