Release history

Changelog

Bug fixes

• Fixed a bug in under_sampling.NearMiss version 3. The indices returned were wrong. By Guillaume Lemaitre.
• fixed bug for ensemble.BalanceCascade and combine.SMOTEENN and :class:`SMOTETomek. By `Guillaume Lemaitre`_.`

New features

• 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. By Guillaume Lemaitre.

Enhancement

• All the unit tests have been factorized and a check_estimators has been derived from scikit-learn. By Guillaume Lemaitre.
• Script for automatic build of conda packages and uploading. By Guillaume Lemaitre
• Remove seaborn dependence and improve the examples. By Guillaume Lemaitre.
• adapt all classes to multi-class resampling. By Guillaume Lemaitre

API changes summary

• __init__ has been removed from the base.SamplerMixin to create a real mixin class. By Guillaume Lemaitre.
• creation of a module exceptions to handle consistant raising of errors. By Guillaume Lemaitre.
• creation of a module utils.validation to make checking of recurrent patterns. By Guillaume Lemaitre.
• move the under-sampling methods in prototype_selection and prototype_generation submodule to make a clearer dinstinction. By Guillaume Lemaitre.
• change ratio such that it can adapt to multiple class problems. By Guillaume Lemaitre.

Deprecation

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

Version 0.2

Changelog

Bug fixes

• 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. By Christos Aridas .
• Fixed a bug in pipeline.Pipeline, solve the issue to put to sampler in the same Pipeline. 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. 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. By Rafael Wampfler.

New features

• Added AllKNN under sampling technique. By Dayvid Oliveira.
• Added a module metrics implementing some specific scoring function for the problem of balancing. By Guillaume Lemaitre and Christos Aridas.

Enhancement

• 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.

API changes summary

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

Documentation changes

• Replace some remaining UnbalancedDataset occurences. By Francois Magimel.
• Added doctest in the documentation. By Guillaume Lemaitre.

Version 0.1

Changelog

API

• First release of the stable API. By Fernando Nogueira, Guillaume Lemaitre, Christos Aridas, and Dayvid Oliveira.

New methods

• 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