From 9df7b06afba830b0e8e6f5834869623912ae9ce9 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Thu, 30 Mar 2017 09:56:07 +0200 Subject: [PATCH] DOC: Update README (#278) --- README.rst | 70 +++++++++++++++++++++++++++++---------------------- doc/about.rst | 18 ++++++------- 2 files changed, 49 insertions(+), 39 deletions(-) diff --git a/README.rst b/README.rst index ed296e3a9..da2f21e03 100644 --- a/README.rst +++ b/README.rst @@ -1,13 +1,5 @@ .. -*- mode: rst -*- -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. - .. _scikit-learn: http://scikit-learn.org/stable/ .. _scikit-learn-contrib: https://github.com/scikit-learn-contrib @@ -41,8 +33,16 @@ projects. .. |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 +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_. @@ -50,19 +50,22 @@ documentation_. .. _documentation: http://contrib.scikit-learn.org/imbalanced-learn/ Installation -============ +------------ Dependencies ------------- +~~~~~~~~~~~~ -imbalanced-learn is tested to work under Python 2.7 and Python 3.5. +imbalanced-learn is tested to work under Python 2.7 and Python 3.4, +3.5, and 3.6. * scipy(>=0.18.1) * numpy(>=1.11.2) * scikit-learn(>=0.18.1) +Additionally, to run the examples, you need matplotlib(>=2.0.0). + Installation ------------- +~~~~~~~~~~~~ imbalanced-learn is currently available on the PyPi's repository and you can install it via `pip`:: @@ -81,14 +84,36 @@ commands to get a copy from GitHub and install all dependencies:: pip install . Testing -------- +~~~~~~~ After installation, you can use `nose` 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 +`_. + 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.html} + } Most classification algorithms will only perform optimally when the number of samples of each class is roughly the same. Highly skewed datasets, where the @@ -139,21 +164,6 @@ The different algorithms are presented in the sphinx-gallery_. .. _sphinx-gallery: http://contrib.scikit-learn.org/imbalanced-learn/auto_examples/index.html -This is a work in progress. Any comments, suggestions or corrections are welcome. - -If you use imbalanced-learn in a scientific publication, we would appreciate -citations to the following paper:: - - @article{lemaitre2016imbalanced, - 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 = {CoRR}, - volume = {abs/1609.06570}, - year = {2016}, - url = {http://arxiv.org/abs/1609.06570} - } References: ----------- diff --git a/doc/about.rst b/doc/about.rst index 733262292..56773d4be 100644 --- a/doc/about.rst +++ b/doc/about.rst @@ -11,13 +11,13 @@ Citing imbalanced-learn If you use imbalanced-learn in a scientific publication, we would appreciate citations to the following paper:: - @article{lemaitre2016imbalanced, - 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 = {CoRR}, - volume = {abs/1609.06570}, - year = {2016}, - url = {http://arxiv.org/abs/1609.06570} + @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.html} }