Changelog

Bug fixes

• Fix the way we check for a specific Python version in the test suite. #1075 by Guillaume Lemaitre.

Changelog

Bug fixes

• Fix a bug in InstanceHardnessThreshold where estimator could not be a Pipeline object. #1049 by Gonenc Mogol.

Compatibility

• Do not use distutils in tests due to deprecation. #1065 by Michael R. Crusoe.
• Fix the scikit-learn import in tests to be compatible with version 1.4.1.post1. #1073 by Guillaume Lemaitre.
• Fix test to be compatible with Python 3.13. #1073 by Guillaume Lemaitre.

Changelog

Bug fixes

• Fix a bug in SMOTENC where the entries of the one-hot encoding should be divided by sqrt(2) and not 2, taking into account that they are plugged into an Euclidean distance computation. #1014 by Guillaume Lemaitre.
• Raise an informative error message when all support vectors are tagged as noise in SVMSMOTE. #1016 by Guillaume Lemaitre.
• Fix a bug in SMOTENC where the median of standard deviation of the continuous features was only computed on the minority class. Now, we are computing this statistic for each class that is up-sampled. #1015 by Guillaume Lemaitre.
• Fix a bug in SMOTENC such that the case where the median of standard deviation of the continuous features is null is handled in the multiclass case as well. #1015 by Guillaume Lemaitre.
• Fix a bug in BorderlineSMOTE version 2 where samples should be generated from the whole dataset and not only from the minority class. #1023 by Guillaume Lemaitre.
• Fix a bug in NeighbourhoodCleaningRule where the kind_sel="all" was not working as explained in the literature. #1012 by Guillaume Lemaitre.
• Fix a bug in NeighbourhoodCleaningRule where the threshold_cleaning ratio was multiplied on the total number of samples instead of the number of samples in the minority class. #1012 by Guillaume Lemaitre.
• Fix a bug in RandomUnderSampler and RandomOverSampler where a column containing only NaT was not handled correctly. #1059 by Guillaume Lemaitre.

Compatibility

• BalancedRandomForestClassifier now support missing values and monotonic constraints if scikit-learn >= 1.4 is installed.
• Pipeline support metadata routing if scikit-learn >= 1.4 is installed.
• Compatibility with scikit-learn 1.4. #1058 by Guillaume Lemaitre.

Deprecations

• Deprecate estimator_ argument in favor of estimators_ for the classes CondensedNearestNeighbour and OneSidedSelection. estimator_ will be removed in 0.14. #1011 by Guillaume Lemaitre.
• Deprecate kind_sel in #1012 by Guillaume Lemaitre.

Enhancements

• Allows to output dataframe with sparse format if provided as input. #1059 by ts2095.

Changelog

Bug fixes

• Fix a bug in classification_report_imbalanced where the parameter target_names was not taken into account when output_dict=True. #989 by AYY7.

• SMOTENC now handles mix types of data type such as bool and pd.CategoricalDtype by delegating the conversion to scikit-learn encoder. #1002 by Guillaume Lemaitre.

• Handle sparse matrices in SMOTEN and raise a warning since it requires a conversion to dense matrices. #1003 by Guillaume Lemaitre.

• Remove spurious warning raised when minority class get over-sampled more than the number of sample in the majority class. #1007 by Guillaume Lemaitre.

Compatibility

• Maintenance release for being compatible with scikit-learn >= 1.3.0. #999 by Guillaume Lemaitre.

Deprecation

• The fitted attribute ohe_ in SMOTENC is deprecated and will be removed in version 0.13. Use categorical_encoder_ instead. #1000 by Guillaume Lemaitre.

• The default of the parameters sampling_strategy and replacement will change in BalancedRandomForestClassifier to follow the implementation of the original paper. This changes will take effect in version 0.13. #1006 by Guillaume Lemaitre.

Enhancements

• SMOTENC now accepts a parameter categorical_encoder allowing to specify a OneHotEncoder with custom parameters. #1000 by Guillaume Lemaitre.

• SMOTEN now accepts a parameter categorical_encoder allowing to specify a OrdinalEncoder with custom parameters. A new fitted parameter categorical_encoder_ is exposed to access the fitted encoder. #1001 by Guillaume Lemaitre.

• RandomUnderSampler and RandomOverSampler (when shrinkage is not None) now accept any data types and will not attempt any data conversion. #1004 by Guillaume Lemaitre.

• SMOTENC now support passing array-like of str when passing the categorical_features parameter. #1008 by :userGuillaume Lemaitre <glemaitre>.

• SMOTENC now support automatic categorical inference when categorical_features is set to "auto". #1009 by :userGuillaume Lemaitre <glemaitre>.

Changelog

Bug fixes

• 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 bu Guillaume Lemaitre.

Compatibility

• Maintenance release for being 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.

Compatibility with scikit-learn 1.1.0

Compatibility with scikit-learn 1.0.2

Version 0.8.1

September 29, 2021

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 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.