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.

0.4.0

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 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.

0.3.4

Just for switching documentation

0.3.3

Bug fix in the classification report

0.3.2

bumpversion 0.3.2

0.3.1

Minor documentation revisions

0.3.0

What's new in version 0.3.0

Testing

• Pytest is used instead of nosetests. :issue:321 by Joan Massich_.

Documentation

• Added a User Guide and extended some examples. :issue:295 by Guillaume Lemaitre_.

Bug fixes

• Fixed a bug in :func:utils.check_ratio such that an error is raised when
  the number of samples required is negative. :issue:312 by Guillaume Lemaitre_.

• Fixed a bug in :class:under_sampling.NearMiss version 3. The
  indices returned were wrong. :issue:312 by Guillaume Lemaitre_.

• Fixed bug for :class:ensemble.BalanceCascade and :class:combine.SMOTEENN
  and :class:SMOTETomek. :issue:295 by Guillaume Lemaitre_.`

• Fixed bug for check_ratio to be able to pass arguments when ratio is a
  callable. :issue:307 by Guillaume Lemaitre_.`

New features

• Turn off steps in :class:pipeline.Pipeline using the None
  object. By Christos Aridas_.

• Add a fetching function :func:datasets.fetch_datasets in order to get some
  imbalanced datasets useful for benchmarking. :issue:249 by Guillaume Lemaitre_.

Enhancement

• All samplers accepts sparse matrices with defaulting on CSR type. :issue:316 by
  Guillaume Lemaitre_.

• :func:datasets.make_imbalance take a ratio similarly to other samplers. It
  supports multiclass. :issue:312 by Guillaume Lemaitre_.

• All the unit tests have been factorized and a :func:utils.check_estimators
  has been derived from scikit-learn. By Guillaume Lemaitre_.

• Script for automatic build of conda packages and uploading. :issue:242 by
  Guillaume Lemaitre_

• Remove seaborn dependence and improve the examples. :issue:264 by Guillaume Lemaitre_.

• adapt all classes to multi-class resampling. :issue:290 by Guillaume Lemaitre_

API changes summary

• __init__ has been removed from the :class:base.SamplerMixin to
  create a real mixin class. :issue:242 by Guillaume Lemaitre_.

• creation of a module :mod:exceptions to handle consistant raising of
  errors. :issue:242 by Guillaume Lemaitre_.

• creation of a module utils.validation to make checking of
  recurrent patterns. :issue:242 by Guillaume Lemaitre_.

• move the under-sampling methods in prototype_selection and
  prototype_generation submodule to make a clearer dinstinction. :issue:277 by
  Guillaume Lemaitre_.

• change ratio such that it can adapt to multiple class problems. :issue:290 by
  Guillaume Lemaitre_.

Deprecation

• Deprecation of the use of min_c_ in :func:datasets.make_imbalance. :issue:312 by
  Guillaume Lemaitre_

• Deprecation of the use of float in :func:datasets.make_imbalance for the
  ratio parameter. :issue:290 by Guillaume Lemaitre_.

• deprecate the use of float as ratio in favor of dictionary, string, or
  callable. :issue:290 by Guillaume Lemaitre_.

0.2.1

release 0.2.1

0.2.0

Release 0.2.0