imbalanced库的安装

Happy清子

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.

Installation Dependencies

imbalanced-learn is tested to work under Python 2.7 and Python 3.6, and 3.7. The dependency requirements are based on the last scikit-learn release:

• scipy(>=0.13.3)
• numpy(>=1.8.2)
• scikit-learn(>=0.20)
• keras 2 (optional)
• tensorflow (optional)
  

Additionally, to run the examples, you need matplotlib(>=2.0.0) and pandas(>=0.22).

imbalanced-learn 0.4 is the last version to support Python 2.7

Installation

imbalanced-learn is currently available on the PyPi’s repository 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

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.gitcd imbalanced-learnpip install .

Or install using pip and GitHub:

pip install -U git+https://github.com/scikit-learn-contrib/imbalanced-learn.git

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.

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}}

shaoheshaohe

这个库
先在用的多

shaoheshaohe

希望平台里面，
预装库，要越来越多地