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.
Installation
Dependencies
imbalanced-learn is tested to work under Python 3.6+. The dependency requirements are based on the last scikit-learn release:
scipy(>=0.17)
numpy(>=1.11)
scikit-learn(>=0.22)
joblib(>=0.11)
keras 2 (optional)
tensorflow (optional)
Additionally, to run the examples, you need matplotlib(>=2.0.0) and pandas(>=0.22).
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.git cd imbalanced-learn pip 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}
}
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.
- Re-sampling techniques are divided in two categories:
Under-sampling the majority class(es).
Over-sampling the minority class.
Combining over- and under-sampling.
Create ensemble balanced sets.
Below is a list of the methods currently implemented in this module.
- Under-sampling
Random majority under-sampling with replacement
Extraction of majority-minority Tomek links [1]
Under-sampling with Cluster Centroids
NearMiss-(1 & 2 & 3) [2]
Condensed Nearest Neighbour [3]
One-Sided Selection [4]
Neighboorhood Cleaning Rule [5]
Edited Nearest Neighbours [6]
Instance Hardness Threshold [7]
Repeated Edited Nearest Neighbours [14]
AllKNN [14]
- Over-sampling
Random minority over-sampling with replacement
SMOTE - Synthetic Minority Over-sampling Technique [8]
SMOTENC - SMOTE for Nominal Continuous [8]
bSMOTE(1 & 2) - Borderline SMOTE of types 1 and 2 [9]
SVM SMOTE - Support Vectors SMOTE [10]
ADASYN - Adaptive synthetic sampling approach for imbalanced learning [15]
KMeans-SMOTE [17]
Mini-batch resampling for Keras and Tensorflow
The different algorithms are presented in the sphinx-gallery.
References:
1 maintainer
Release files for imbalanced-learn 0.6.0
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| imbalanced_learn-0.6.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 339.2 kB
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