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Toolbox for imbalanced dataset in machine learning.

Project description

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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 requires the following dependencies:

  • Python (>= 3.8)

  • NumPy (>= 1.17.3)

  • SciPy (>= 1.3.2)

  • Scikit-learn (>= 1.1.0)

Additionally, imbalanced-learn requires the following optional dependencies:

  • Pandas (>= 1.0.5) for dealing with dataframes

  • Tensorflow (>= 2.4.3) for dealing with TensorFlow models

  • Keras (>= 2.4.3) for dealing with Keras models

The examples will requires the following additional dependencies:

  • Matplotlib (>= 3.1.2)

  • Seaborn (>= 0.9.0)

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.

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:
  1. Under-sampling the majority class(es).

  2. Over-sampling the minority class.

  3. Combining over- and under-sampling.

  4. Create ensemble balanced sets.

Below is a list of the methods currently implemented in this module.

  • Under-sampling
    1. Random majority under-sampling with replacement

    2. Extraction of majority-minority Tomek links [1]

    3. Under-sampling with Cluster Centroids

    4. NearMiss-(1 & 2 & 3) [2]

    5. Condensed Nearest Neighbour [3]

    6. One-Sided Selection [4]

    7. Neighboorhood Cleaning Rule [5]

    8. Edited Nearest Neighbours [6]

    9. Instance Hardness Threshold [7]

    10. Repeated Edited Nearest Neighbours [14]

    11. AllKNN [14]

  • Over-sampling
    1. Random minority over-sampling with replacement

    2. SMOTE - Synthetic Minority Over-sampling Technique [8]

    3. SMOTENC - SMOTE for Nominal and Continuous [8]

    4. SMOTEN - SMOTE for Nominal [8]

    5. bSMOTE(1 & 2) - Borderline SMOTE of types 1 and 2 [9]

    6. SVM SMOTE - Support Vectors SMOTE [10]

    7. ADASYN - Adaptive synthetic sampling approach for imbalanced learning [15]

    8. KMeans-SMOTE [17]

    9. ROSE - Random OverSampling Examples [19]

  • Over-sampling followed by under-sampling
    1. SMOTE + Tomek links [12]

    2. SMOTE + ENN [11]

  • Ensemble classifier using samplers internally
    1. Easy Ensemble classifier [13]

    2. Balanced Random Forest [16]

    3. Balanced Bagging

    4. RUSBoost [18]

  • Mini-batch resampling for Keras and Tensorflow

The different algorithms are presented in the sphinx-gallery.

References:

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