Imbalanced-learn

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.

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.

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>`.

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>`.

=============

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>`.

Mainly bugfix in SMOTE NC