Imbalanced-learn

Changelog
========

Bug fixes
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- Fix a regression in over-sampler where the string `minority` was rejected as an unvalid sampling strategy. 964 by Prakhyath07.

Changelog
========

Bug fixes
---------

- Make sure that Substitution is working with `python -OO` that replaces __doc__ by None. [953](https://github.com/scikit-learn-contrib/imbalanced-learn/pull/953) bu [Guillaume Lemaitre](https://github.com/glemaitre).

Compatibility
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- Maintenance release for being compatible with scikit-learn >= 1.0.2. [946](https://github.com/scikit-learn-contrib/imbalanced-learn/pull/946), [#947](https://github.com/scikit-learn-contrib/imbalanced-learn/pull/947), [#949](https://github.com/scikit-learn-contrib/imbalanced-learn/pull/949) by [Guillaume Lemaitre](https://github.com/glemaitre).
- Add support for automatic parameters validation as in scikit-learn >= 1.2. [955](https://github.com/scikit-learn-contrib/imbalanced-learn/pull/955) by [Guillaume Lemaitre](https://github.com/glemaitre).
- Add support for `feature_names_in_` as well as `get_feature_names_out` for all samplers. [959](https://github.com/scikit-learn-contrib/imbalanced-learn/pull/959) by [Guillaume Lemaitre](https://github.com/glemaitre).

Deprecation
------------

- The parameter `n_jobs` has been deprecated from the classes [ADASYN](https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.ADASYN.html#imblearn.over_sampling.ADASYN), [BorderlineSMOTE](https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.BorderlineSMOTE.html#imblearn.over_sampling.BorderlineSMOTE), [SMOTE](https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html#imblearn.over_sampling.SMOTE), [SMOTENC](https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTENC.html#imblearn.over_sampling.SMOTENC), [SMOTEN](https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTEN.html#imblearn.over_sampling.SMOTEN), and [SVMSMOTE](https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SVMSMOTE.html#imblearn.over_sampling.SVMSMOTE). Instead, pass a nearest neighbors estimator where n_jobs is set. [887](https://github.com/scikit-learn-contrib/imbalanced-learn/pull/887) by [Guillaume Lemaitre](https://github.com/glemaitre).
- The parameter `base_estimator` is deprecated and will be removed in version 0.12. It is impacted the following classes: [BalancedBaggingClassifier](https://imbalanced-learn.org/dev/references/generated/imblearn.ensemble.BalancedBaggingClassifier.html#imblearn.ensemble.BalancedBaggingClassifier), [EasyEnsembleClassifier](https://imbalanced-learn.org/dev/references/generated/imblearn.ensemble.EasyEnsembleClassifier.html#imblearn.ensemble.EasyEnsembleClassifier), [RUSBoostClassifier](https://imbalanced-learn.org/dev/references/generated/imblearn.ensemble.RUSBoostClassifier.html#imblearn.ensemble.RUSBoostClassifier). [946](https://github.com/scikit-learn-contrib/imbalanced-learn/pull/946) by [Guillaume Lemaitre](https://github.com/glemaitre).

Enhancements
---------------

- Add support to accept compatible NearestNeighbors objects by only duck-typing. For instance, it allows to accept cuML instances. [858](https://github.com/scikit-learn-contrib/imbalanced-learn/pull/858) by [NV-jpt](https://github.com/NV-jpt) and [Guillaume Lemaitre](https://github.com/glemaitre).

September 29, 2021

Maintenance

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

**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 controled 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.