Version 0.8.1

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

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.

Version 0.7.0

A release to bump the minimum version of scikit-learn to 0.23 with a couple of bug fixes.
Check the what's new for more information.

Version 0.6.2

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

Version 0.6.1

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 :class: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. :pr:656 by :user:Guillaume Lemaitre <glemaitre>.

Version 0.6.0

Changelog

Changed models
..............

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

• :class:imblearn.under_sampling.ClusterCentroids
• :class:imblearn.under_sampling.InstanceHardnessThreshold

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

• :class:imblearn.over_sampling.SMOTENC

Bug fixes
.........

• :class: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.
  :pr:599 by :user:Shihab Shahriar Khan <Shihab-Shahriar>.

• Fix a bug in :class:imblearn.ensemble.BalancedRandomForestClassifier
  leading to a wrong computation of the OOB score.
  :pr:656 by :user:Guillaume Lemaitre <glemaitre>.

Maintenance
...........

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

• Synchronize :mod:imblearn.pipeline with :mod:sklearn.pipeline.
  :pr:620 by :user:Guillaume Lemaitre <glemaitre>.

• Synchronize :class:imblearn.ensemble.BalancedRandomForestClassifier and add
  parameters max_samples and ccp_alpha.
  :pr:621 by :user:Guillaume Lemaitre <glemaitre>.

Enhancement
...........

• :class:imblearn.under_sampling.RandomUnderSampling,
  :class:imblearn.over_sampling.RandomOverSampling,
  :class: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.
  :pr:636 by :user:Guillaume Lemaitre <glemaitre>.

• :class:imblearn.FunctionSampler accepts a parameter validate allowing
  to check or not the input X and y.
  :pr:637 by :user:Guillaume Lemaitre <glemaitre>.

• :class:imblearn.under_sampling.RandomUnderSampler,
  :class:imblearn.over_sampling.RandomOverSampler can resample when non
  finite values are present in X.
  :pr:643 by :user:Guillaume Lemaitre <glemaitre>.

• All samplers will output a Pandas DataFrame if a Pandas DataFrame was given
  as an input.
  :pr:644 by :user:Guillaume Lemaitre <glemaitre>.

• The samples generation in
  :class:imblearn.over_sampling.SMOTE,
  :class:imblearn.over_sampling.BorderlineSMOTE,
  :class:imblearn.over_sampling.SVMSMOTE,
  :class:imblearn.over_sampling.KMeansSMOTE,
  :class:imblearn.over_sampling.SMOTENC is now vectorize with giving
  an additional speed-up when X in sparse.
  :pr:596 by :user:Matt Eding <MattEding>.

Deprecation
...........

• The following classes have been removed after 2 deprecation cycles:
  ensemble.BalanceCascade and ensemble.EasyEnsemble.
  :pr:617 by :user:Guillaume Lemaitre <glemaitre>.

• The following functions have been removed after 2 deprecation cycles:
  utils.check_ratio.
  :pr:617 by :user:Guillaume Lemaitre <glemaitre>.

• The parameter ratio and return_indices has been removed from all
  samplers.
  :pr:617 by :user:Guillaume Lemaitre <glemaitre>.

• The parameters m_neighbors, out_step, kind, svm_estimator
  have been removed from the :class:imblearn.over_sampling.SMOTE.
  :pr:617 by :user:Guillaume Lemaitre <glemaitre>.

0.5.0

Version 0.5.0

Changed models

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

• :class:imblearn.ensemble.RUSBoostClassifier default estimator changed from
  :class: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.
  :issue:525 by :user:Ariel Rossanigo <arielrossanigo>.

• Add :class:imblearn.over_sampling.BorderlineSMOTE and
  :class:imblearn.over_sampling.SVMSMOTE in the API documenation.
  :issue:530 by :user:Guillaume Lemaitre <glemaitre>.

Enhancement

• Add Parallelisation for SMOTEENN and SMOTETomek.
  :pr:547 by :user:Michael Hsieh <Microsheep>.

• Add :class:imblearn.utils._show_versions. Updated the contribution guide
  and issue template showing how to print system and dependency information
  from the command line. :pr:557 by :user:Alexander L. Hayes <batflyer>.

• Add :class:imblearn.over_sampling.KMeansSMOTE which is an over-sampler
  clustering points before to apply SMOTE.
  :pr:435 by :user:Stephan Heijl <StephanHeijl>.

Maintenance

• Make it possible to import imblearn and access submodule.
  :pr:500 by :user:Guillaume Lemaitre <glemaitre>.

• Remove support for Python 2, remove deprecation warning from
  scikit-learn 0.21.
  :pr:576 by :user:Guillaume Lemaitre <glemaitre>.

Bug

• Fix wrong usage of :class: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.
  :pr:531 by :user:Guillaume Lemaitre <glemaitre>.

• Fix bug which converting to COO format sparse when stacking the matrices in
  :class:imblearn.over_sampling.SMOTENC. This bug was only old scipy version.
  :pr:539 by :user:Guillaume Lemaitre <glemaitre>.

• Fix bug in :class:imblearn.pipeline.Pipeline where None could be the final
  estimator.
  :pr:554 by :user:Oliver Rausch <orausch>.

• Fix bug in :class:imblearn.over_sampling.SVMSMOTE and
  :class:imblearn.over_sampling.BorderlineSMOTE where the default parameter
  of n_neighbors was not set properly.
  :pr:578 by :user:Guillaume Lemaitre <glemaitre>.

• Fix bug by changing the default depth in
  :class:imblearn.ensemble.RUSBoostClassifier to get a decision stump as a
  weak learner as in the original paper.
  :pr:545 by :user:Christos Aridas <chkoar>.

• Allow to import keras directly from tensorflow in the
  :mod:imblearn.keras.
  :pr:531 by :user:Guillaume Lemaitre <glemaitre>.

0.4.3

Mainly bugfix in SMOTE NC

0.4.2

Version 0.4.2

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.

0.4.1

Version 0.4

October, 2018

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.