v0.4.rst

Version 0.4.2

Changelog

Bug fixes

• Fix a bug in :class:`imblearn.over_sampling.SMOTENC` in which the the median of the standard deviation instead of half of the median of the standard deviation. By :user:`Guillaume Lemaitre <glemaitre>` in :issue:`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 :user:`Guillaume Lemaitre <glemaitre>` in :issue:`490`.
• Fix a bug in :class:`imblearn.over_sampling.SMOTENC` in which a sparse matrices were densify during inverse_transform. By :user:`Guillaume Lemaitre <glemaitre>` in :issue:`495`.
• Fix a bug in :class:`imblearn.over_sampling.SMOTE_NC` in which a the tie breaking was wrongly sampling. By :user:`Guillaume Lemaitre <glemaitre>` in :issue:`497`.

Version 0.4

October, 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 :mod:`imblearn.keras` and :mod:`imblearn.tensorflow` have been added in which imbalanced-learn samplers can be used to generate balanced mini-batches.

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

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

The :class:`imblearn.over_sampling.SMOTE` has been simplified and break down to 2 additional classes: :class:`imblearn.over_sampling.SVMSMOTE` and :class:`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. :issue:`411` by :user:`Guillaume Lemaitre <glemaitre>`.
• Enable to use a float with binary classification for sampling_strategy. :issue:`411` by :user:`Guillaume Lemaitre <glemaitre>`.
• Enable to use a list for the cleaning methods to specify the class to sample. :issue:`411` by :user:`Guillaume Lemaitre <glemaitre>`.
• 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. :issue:`462` by :user:`Guillaume Lemaitre <glemaitre>`.

New features

• Add a :mod:`keras` and :mod:`tensorflow` modules to create balanced mini-batches generator. :issue:`409` by :user:`Guillaume Lemaitre <glemaitre>`.
• Add :class:`imblearn.ensemble.EasyEnsembleClassifier` which create a bag of AdaBoost classifier trained on balanced bootstrap samples. :issue:`455` by :user:`Guillaume Lemaitre <glemaitre>`.
• Add :class:`imblearn.ensemble.BalancedRandomForestClassifier` which balanced each bootstrap provided to each tree of the forest. :issue:`459` by :user:`Guillaume Lemaitre <glemaitre>`.
• Add :class:`imblearn.ensemble.RUSBoostClassifier` which applied a random under-sampling stage before each boosting iteration of AdaBoost. :issue:`469` by :user:`Guillaume Lemaitre <glemaitre>`.
• Add :class:`imblern.over_sampling.SMOTENC` which generate synthetic samples on data set with heterogeneous data type (continuous and categorical features). :issue:`412` by :user:`Denis Dudnik <ddudnik>` and :user:`Guillaume Lemaitre <glemaitre>`.

Enhancement

• Add a documentation node to create a balanced random forest from a balanced bagging classifier. :issue:`372` by :user:`Guillaume Lemaitre <glemaitre>`.
• Document the metrics to evaluate models on imbalanced dataset. :issue:`367` by :user:`Guillaume Lemaitre <glemaitre>`.
• Add support for one-vs-all encoded target to support keras. :issue:`409` by :user:`Guillaume Lemaitre <glemaitre>`.
• Adding specific class for borderline and SVM SMOTE using :class:`BorderlineSMOTE` and :class:`SVMSMOTE`. :issue:`440` by :user:`Guillaume Lemaitre <glemaitre>`.
• Allow :class:`imblearn.over_sampling.RandomOverSampler` can return indices using the attributes return_indices. :issue:`439` by :user:`Hugo Gascon<hgascon>` and :user:`Guillaume Lemaitre <glemaitre>`.
• Allow :class:`imblearn.under_sampling.RandomUnderSampler` and :class:`imblearn.over_sampling.RandomOverSampler` to sample object array containing strings. :issue:`451` by :user:`Guillaume Lemaitre <glemaitre>`.

Bug fixes

• Fix bug in :func:`metrics.classification_report_imbalanced` for which y_pred and y_true where inversed. :issue:`394` by :user:`Ole Silvig <klizter>.`
• Fix bug in ADASYN to consider only samples from the current class when generating new samples. :issue:`354` by :user:`Guillaume Lemaitre <glemaitre>`.
• Fix bug which allow for sorted behavior of sampling_strategy dictionary and thus to obtain a deterministic results when using the same random state. :issue:`447` by :user:`Guillaume Lemaitre <glemaitre>`.
• Force to clone scikit-learn estimator passed as attributes to samplers. :issue:`446` by :user:`Guillaume Lemaitre <glemaitre>`.
• Fix bug which was not preserving the dtype of X and y when generating samples. :issue:`450` by :user:`Guillaume Lemaitre <glemaitre>`.
• Add the option to pass a Memory object to :func:`make_pipeline` like in :class:`pipeline.Pipeline` class. :issue:`458` by :user:`Christos Aridas <chkoar>`.

Maintenance

• Remove deprecated parameters in 0.2 - :issue:`331` by :user:`Guillaume Lemaitre <glemaitre>`.
• Make some modules private. :issue:`452` by :user:`Guillaume Lemaitre <glemaitre>`.
• Upgrade requirements to scikit-learn 0.20. :issue:`379` by :user:`Guillaume Lemaitre <glemaitre>`.
• Catch deprecation warning in testing. :issue:`441` by :user:`Guillaume Lemaitre <glemaitre>`.
• Refactor and impose pytest style tests. :issue:`470` by :user:`Guillaume Lemaitre <glemaitre>`.

Documentation

• Remove some docstring which are not necessary. :issue:`454` by :user:`Guillaume Lemaitre <glemaitre>`.
• Fix the documentation of the sampling_strategy parameters when used as a float. :issue:`480` by :user:`Guillaume Lemaitre <glemaitre>`.

Deprecation

• Deprecate ratio in favor of sampling_strategy. :issue:`411` by :user:`Guillaume Lemaitre <glemaitre>`.
• Deprecate the use of a dict for cleaning methods. a list should be used. :issue:`411` by :user:`Guillaume Lemaitre <glemaitre>`.
• Deprecate random_state in :class:`imblearn.under_sampling.NearMiss`, :class:`imblearn.under_sampling.EditedNearestNeighbors`, :class:`imblearn.under_sampling.RepeatedEditedNearestNeighbors`, :class:`imblearn.under_sampling.AllKNN`, :class:`imblearn.under_sampling.NeighbourhoodCleaningRule`, :class:`imblearn.under_sampling.InstanceHardnessThreshold`, :class:`imblearn.under_sampling.CondensedNearestNeighbours`.
• Deprecate kind, out_step, svm_estimator, m_neighbors in :class:`imblearn.over_sampling.SMOTE`. User should use :class:`imblearn.over_sampling.SVMSMOTE` and :class:`imblearn.over_sampling.BorderlineSMOTE`. :issue:`440` by :user:`Guillaume Lemaitre <glemaitre>`.
• Deprecate :class:`imblearn.ensemble.EasyEnsemble` in favor of meta-estimator :class:`imblearn.ensemble.EasyEnsembleClassifier` which follow the exact algorithm described in the literature. :issue:`455` by :user:`Guillaume Lemaitre <glemaitre>`.
• Deprecate :class:`imblearn.ensemble.BalanceCascade`. :issue:`472` by :user:`Guillaume Lemaitre <glemaitre>`.
• Deprecate return_indices in all samplers. Instead, an attribute sample_indices_ is created whenever the sampler is selecting a subset of the original samples. :issue:`474` by :user:`Guillaume Lemaitre <glemaitre`.