imbalanced-learn

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

https://github.com/scikit-learn-contrib/imbalanced-learn

Keywords

data-analysis data-science machine-learning python statistics

Keywords from Contributors

data-mining distributed alignment flexible tensors transformers parallel autograd gbrt gbm

Last synced: 6 months ago · JSON representation

Repository

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

Basic Info

Host: GitHub
Owner: scikit-learn-contrib
License: mit
Language: Python
Default Branch: master
Homepage: https://imbalanced-learn.org
Size: 21.8 MB

Statistics

Stars: 7,036
Watchers: 141
Forks: 1,313
Open Issues: 55
Releases: 40

Topics

data-analysis data-science machine-learning python statistics

Created over 11 years ago · Last pushed 6 months ago

Metadata Files

Readme Contributing License Authors

README.rst

.. -*- mode: rst -*-

.. _scikit-learn: http://scikit-learn.org/stable/

.. _scikit-learn-contrib: https://github.com/scikit-learn-contrib

|GitHubActions|_ |Codecov|_ |CircleCI|_ |PythonVersion|_ |Pypi|_ |Gitter|_ |Black|_

.. |GitHubActions| image:: https://github.com/scikit-learn-contrib/imbalanced-learn/actions/workflows/tests.yml/badge.svg
.. _GitHubActions: https://github.com/scikit-learn-contrib/imbalanced-learn/actions/workflows/tests.yml

.. |Codecov| image:: https://codecov.io/gh/scikit-learn-contrib/imbalanced-learn/branch/master/graph/badge.svg
.. _Codecov: https://codecov.io/gh/scikit-learn-contrib/imbalanced-learn

.. |CircleCI| image:: https://circleci.com/gh/scikit-learn-contrib/imbalanced-learn.svg?style=shield
.. _CircleCI: https://circleci.com/gh/scikit-learn-contrib/imbalanced-learn/tree/master

.. |PythonVersion| image:: https://img.shields.io/pypi/pyversions/imbalanced-learn.svg
.. _PythonVersion: https://img.shields.io/pypi/pyversions/imbalanced-learn.svg

.. |Pypi| image:: https://badge.fury.io/py/imbalanced-learn.svg
.. _Pypi: https://badge.fury.io/py/imbalanced-learn

.. |Gitter| image:: https://badges.gitter.im/scikit-learn-contrib/imbalanced-learn.svg
.. _Gitter: https://gitter.im/scikit-learn-contrib/imbalanced-learn?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge

.. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
.. _Black: :target: https://github.com/psf/black

.. |PythonMinVersion| replace:: 3.10
.. |NumPyMinVersion| replace:: 1.25.2
.. |SciPyMinVersion| replace:: 1.11.4
.. |ScikitLearnMinVersion| replace:: 1.4.2
.. |MatplotlibMinVersion| replace:: 3.7.3
.. |PandasMinVersion| replace:: 2.0.3
.. |TensorflowMinVersion| replace:: 2.16.1
.. |KerasMinVersion| replace:: 3.3.3
.. |SeabornMinVersion| replace:: 0.12.2
.. |PytestMinVersion| replace:: 7.2.2

imbalanced-learn
================

imbalanced-learn is a python package offering a number of re-sampling techniques
commonly used in datasets showing strong between-class imbalance.
It is compatible with scikit-learn_ and is part of scikit-learn-contrib_
projects.

Documentation
-------------

Installation documentation, API documentation, and examples can be found on the
documentation_.

.. _documentation: https://imbalanced-learn.org/stable/

Installation
------------

Dependencies
~~~~~~~~~~~~

`imbalanced-learn` requires the following dependencies:

- Python (>= |PythonMinVersion|)
- NumPy (>= |NumPyMinVersion|)
- SciPy (>= |SciPyMinVersion|)
- Scikit-learn (>= |ScikitLearnMinVersion|)
- Pytest (>= |PytestMinVersion|)

Additionally, `imbalanced-learn` requires the following optional dependencies:

- Pandas (>= |PandasMinVersion|) for dealing with dataframes
- Tensorflow (>= |TensorflowMinVersion|) for dealing with TensorFlow models
- Keras (>= |KerasMinVersion|) for dealing with Keras models

The examples will requires the following additional dependencies:

- Matplotlib (>= |MatplotlibMinVersion|)
- Seaborn (>= |SeabornMinVersion|)

Installation
~~~~~~~~~~~~

From PyPi or conda-forge repositories
.....................................

imbalanced-learn is currently available on the PyPi's repositories and you can
install it via `pip`::

  pip install -U imbalanced-learn

The package is release also in Anaconda Cloud platform::

  conda install -c conda-forge imbalanced-learn

From source available on GitHub
...............................

If you prefer, you can clone it and run the setup.py file. Use the following
commands to get a copy from Github and install all dependencies::

  git clone https://github.com/scikit-learn-contrib/imbalanced-learn.git
  cd imbalanced-learn
  pip install .

Be aware that you can install in developer mode with::

  pip install --no-build-isolation --editable .

If you wish to make pull-requests on GitHub, we advise you to install
pre-commit::

  pip install pre-commit
  pre-commit install

Testing
~~~~~~~

After installation, you can use `pytest` to run the test suite::

  make coverage

Development
-----------

The development of this scikit-learn-contrib is in line with the one
of the scikit-learn community. Therefore, you can refer to their
`Development Guide
`_.

Endorsement of the Scientific Python Specification
--------------------------------------------------

We endorse good practices from the Scientific Python Ecosystem Coordination (SPEC).
The full list of recommendations is available `here`_.

See below the list of recommendations that we endorse for the imbalanced-learn project.

|SPEC 0 — Minimum Supported Dependencies|

.. |SPEC 0 — Minimum Supported Dependencies| image:: https://img.shields.io/badge/SPEC-0-green?labelColor=%23004811&color=%235CA038
   :target: https://scientific-python.org/specs/spec-0000/

.. _here: https://scientific-python.org/specs/

About
-----

If you use imbalanced-learn in a scientific publication, we would appreciate
citations to the following paper::

  @article{JMLR:v18:16-365,
  author  = {Guillaume  Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas},
  title   = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning},
  journal = {Journal of Machine Learning Research},
  year    = {2017},
  volume  = {18},
  number  = {17},
  pages   = {1-5},
  url     = {http://jmlr.org/papers/v18/16-365}
  }

Most classification algorithms will only perform optimally when the number of
samples of each class is roughly the same. Highly skewed datasets, where the
minority is heavily outnumbered by one or more classes, have proven to be a
challenge while at the same time becoming more and more common.

One way of addressing this issue is by re-sampling the dataset as to offset this
imbalance with the hope of arriving at a more robust and fair decision boundary
than you would otherwise.

You can refer to the `imbalanced-learn`_ documentation to find details about
the implemented algorithms.

.. _imbalanced-learn: https://imbalanced-learn.org/stable/user_guide.html

Owner

Name: scikit-learn-contrib
Login: scikit-learn-contrib
Kind: organization

Website: http://contrib.scikit-learn.org
Repositories: 27
Profile: https://github.com/scikit-learn-contrib

scikit-learn compatible projects

GitHub Events

Total

Create event: 22
Release event: 2
Issues event: 22
Watch event: 209
Delete event: 20
Issue comment event: 54
Push event: 45
Pull request review event: 9
Pull request review comment event: 7
Pull request event: 52
Fork event: 39

Last Year

Create event: 22
Release event: 2
Issues event: 22
Watch event: 209
Delete event: 20
Issue comment event: 54
Push event: 45
Pull request review event: 9
Pull request review comment event: 7
Pull request event: 52
Fork event: 39

Committers

Last synced: 9 months ago

All Time

Total Commits: 833
Total Committers: 87
Avg Commits per committer: 9.575
Development Distribution Score (DDS): 0.509

Past Year

Commits: 25
Committers: 4
Avg Commits per committer: 6.25
Development Distribution Score (DDS): 0.36

Top Committers

Name	Email	Commits
Guillaume Lemaitre	g**8@g**m	409
Guillaume Lemaitre	g**e@v**u	212
Fernando Nogueira	f**a@g**m	37
chkoar	c****r	33
Dayvid Victor	v**o@g**m	20
Soledad Galli	s**i@p**m	8
dependabot[bot]	4****]	7
Alexander L. Hayes	a**r@b**t	6
Joan Massich	m**k@g**m	5
Matt Eding	3****g	5
chkoar	c**r@c**p	4
microsheep	t**p@g**m	4
T.Thost	5****t	2
Jeff Hale	d****r	2
Shihab Shahriar	r**5@g**m	2
bganglia	4****a	2
Hisashi Osanai	o****i	2
Francis T. O'Donovan	f**n@g**m	2
Yann Bayle	b**4@g**m	2
chkoar	i**r@g**m	2
David Gasquez	d**z@g**m	1
Chuanzhu Xu	c**u@g**m	1
Christian Kastner	c**k@k**t	1
Camilo Prieto	c**5@h**m	1
Beard	1****d	1
Baltschun ali	a**n@g**m	1
Aurélien Massiot	a**t@g**m	1
Ashwin Mathur	9****l	1
Arunabh	a**m@p**m	1
Sebastian Raschka	m**l@s**m	1
and 57 more...

Committer Domains (Top 20 + Academic)

alumni.enseeiht.fr: 1 aridas.eu: 1 schulz.family: 1 uw.edu: 1 cs.put.poznan.pl: 1 ezdi.us: 1 iress.com: 1 gitter.im: 1 wampfler.net: 1 email.ulster.ac.uk: 1 mail.ru: 1 ucsd.edu: 1 samueltaylor.org: 1 sebastianraschka.com: 1 kvr.at: 1 batflyer.net: 1 visor.udg.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 146
Total pull requests: 167
Average time to close issues: 6 months
Average time to close pull requests: about 1 month
Total issue authors: 129
Total pull request authors: 48
Average comments per issue: 2.87
Average comments per pull request: 1.63
Merged pull requests: 111
Bot issues: 0
Bot pull requests: 26

Past Year

Issues: 19
Pull requests: 49
Average time to close issues: 14 days
Average time to close pull requests: 8 days
Issue authors: 19
Pull request authors: 9
Average comments per issue: 1.63
Average comments per pull request: 0.55
Merged pull requests: 24
Bot issues: 0
Bot pull requests: 25

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Top Authors

Issue Authors

glemaitre (8)
solegalli (6)
BrandonKMLee (2)
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tomateit (1)
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iki77 (1)
dcriado1985 (1)
tamargrey (1)
ts2095 (1)
stephengmatthews (1)

Pull Request Authors

glemaitre (76)
dependabot[bot] (29)
solegalli (13)
chkoar (4)
virchan (4)
Ab2nour (2)
neal301 (2)
imgremlin (2)
tthost (2)
fritshermans (2)
AYY7 (2)
ts2095 (2)
gmogol (2)
mr-c (2)
hayesall (2)

Top Labels

Issue Labels

Type: Question (9) Type: Bug (6) good first issue (6) Type: Enhancement (6) Status: More Info Needed (4) For: Documentation (3) Package: keras (1) Package: over_sampling (1) Type: Performance (1) easy (1) Status: Help Wanted (1) Package: under_sampling (1)

Pull Request Labels

dependencies (29) github_actions (12) No Changelog Needed (2) Status: Stalled (1)

Packages

Total packages: 5
Total downloads:
- pypi 16,349,874 last-month
Total docker downloads: 14,006,826

Total dependent packages: 171
(may contain duplicates)
Total dependent repositories: 3,053
(may contain duplicates)
Total versions: 60
Total maintainers: 3

pypi.org: imbalanced-learn

Toolbox for imbalanced dataset in machine learning

Homepage: https://imbalanced-learn.org/
Documentation: https://imbalanced-learn.readthedocs.io/
License: MIT License
Latest release: 0.14.0
published 6 months ago

Versions: 38
Dependent Packages: 158
Dependent Repositories: 2,817
Downloads: 16,349,874 Last month
Docker Downloads: 14,006,826

Rankings

Downloads: 0.1%

Dependent packages count: 0.2%

Dependent repos count: 0.2%

Stargazers count: 0.3%

Average: 0.6%

Forks count: 1.2%

Docker downloads count: 1.4%

Maintainers (1)

glemaitre

Last synced: 6 months ago

alpine-v3.17: py3-imbalanced-learn

Toolbox for imbalanced dataset in machine learning

Homepage: https://github.com/scikit-learn-contrib/imbalanced-learn
License: MIT
Latest release: 0.9.1-r0
published over 3 years ago

Versions: 1
Dependent Packages: 2
Dependent Repositories: 0

Rankings

Dependent repos count: 0.0%

Forks count: 1.4%

Stargazers count: 2.3%

Average: 4.1%

Dependent packages count: 12.7%

Maintainers (1)

iztok@iztok-jr-fister.eu

Last synced: 6 months ago

conda-forge.org: imbalanced-learn

imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance. It is compatible with scikit-learn and is part of scikit-learn-contrib projects.

Homepage: https://github.com/scikit-learn-contrib/imbalanced-learn
License: MIT
Latest release: 0.9.1
published almost 4 years ago

Versions: 15
Dependent Packages: 8
Dependent Repositories: 118

Rankings

Dependent repos count: 3.1%

Forks count: 4.3%

Stargazers count: 4.3%

Average: 4.7%

Dependent packages count: 7.1%

Last synced: 6 months ago

spack.io: py-imbalanced-learn

imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance. It is compatible with scikit-learn and is part of scikit- learn-contrib projects.

Homepage: https://github.com/scikit-learn-contrib/imbalanced-learn
License: []
Latest release: 0.10.1
published almost 3 years ago

Versions: 1
Dependent Packages: 1
Dependent Repositories: 0

Rankings

Dependent repos count: 0.0%

Stargazers count: 1.7%

Forks count: 2.5%

Average: 15.4%

Dependent packages count: 57.3%

Maintainers (1)

meyersbs

Last synced: 6 months ago

anaconda.org: imbalanced-learn

imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance. It is compatible with scikit-learn and is part of scikit-learn-contrib projects.

Homepage: https://imbalanced-learn.org
License: MIT
Latest release: 0.14.0
published 6 months ago

Versions: 5
Dependent Packages: 2
Dependent Repositories: 118

Rankings

Forks count: 10.0%

Stargazers count: 10.3%

Dependent repos count: 17.6%

Average: 19.7%

Dependent packages count: 41.0%

Last synced: 6 months ago

imbalanced-learn

Science Score: 36.0%

Keywords

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Repository

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README.rst

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