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.. _changes_0_4:
Version 0.4.2
=============
**October 21, 2018**
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 12, 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`.
|
