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subgroups - A Python library for Subgroup Discovery

Tests
Package
Metadata

What is it?

subgroups is a public, accessible and open-source python library created to work with the Subgroup Discovery (SD) technique. This library implements the necessary components related to the SD technique and contains a collection of SD algorithms and other data analysis utilities.

Quick install

The easiest way to obtain this library is from either PyPI (the Python Package Index) or Conda.

PyPI

For that, you can view and download the package from its PyPI page or directly install it by executing:

pip install subgroups

Conda

For that, you can view and download the package from its Anaconda.org page (conda-forge channel) or directly install it by executing:

conda install -c conda-forge subgroups

Testing

After installing the library, a collection of tests can be launched by executing:

import subgroups.tests as st
st.run_all_tests()

These tests verify that the library is correctly installed and that all components, algorithms and features are properly working.

Installing from source

The source code (latest development) is currently hosted on: https://github.com/antoniolopezmc/subgroups

Therefore, you need first to clone the repository:

git clone https://github.com/antoniolopezmc/subgroups.git
cd subgroups

After that, the library can be installed in production mode or in develop mode.

Production mode

make install_prod

or

python -m pip install ./

or

pip install ./

This mode installs the library as normal, copying it to the standard Python site-packages directory.

Develop mode

make install_dev

or

python -m pip install -e ./

or

pip install -e ./

This mode installs the library in editable mode, creating a link in the standard Python site-packages directory to the downloaded project directory (the current directory). See the pip_install documentation for further details.

Example of use of the algorithms

An example of use of each algorithm implemented in subgroups python library can be found in the examples/algorithms folder:

• SDMap
• SDMap*
• VLSD
• BSD-CBSD-CPBSD
• QFinder
• GMSL
• DSLM

Documentation

The official documentation is hosted on https://www.um.es/subgroups/

Additionally, the source code of the project contains a folder called docs, which includes the documentation of the library. This documentation can be also manually generated by executing:

cd docs
make build

or

cd docs
python clean.py source/project_files build
python -m pip install --upgrade sphinx
python -m pip install --upgrade sphinx-rtd-theme
python -m pip install --upgrade sphinx-autodoc-typehints
sphinx-apidoc -f -T -M -o source/project_files ../src/subgroups
sphinx-build -M html source build

The generated documentation will be located in the build subfolder.

Citation

If you use subgroups library in a scientific publication, please cite the following paper:

• BibTeX format:

@article{LOPEZMARTINEZCARRASCO2024101895,
title = {Subgroups: A Python library for Subgroup Discovery},
journal = {SoftwareX},
volume = {28},
pages = {101895},
year = {2024},
issn = {2352-7110},
doi = {https://doi.org/10.1016/j.softx.2024.101895},
url = {https://www.sciencedirect.com/science/article/pii/S2352711024002656},
author = {Antonio Lopez-Martinez-Carrasco and Jose M. Juarez and Manuel Campos and Francisco Mora-Caselles},
keywords = {Machine learning, Data mining, Subgroup Discovery, Python}
}

• RIS format:

TY  - JOUR
T1  - Subgroups: A Python library for Subgroup Discovery
AU  - Lopez-Martinez-Carrasco, Antonio
AU  - Juarez, Jose M.
AU  - Campos, Manuel
AU  - Mora-Caselles, Francisco
JO  - SoftwareX
VL  - 28
SP  - 101895
PY  - 2024
DA  - 2024/12/01/
SN  - 2352-7110
DO  - https://doi.org/10.1016/j.softx.2024.101895
UR  - https://www.sciencedirect.com/science/article/pii/S2352711024002656
KW  - Machine learning
KW  - Data mining
KW  - Subgroup Discovery
KW  - Python
ER  - 

• Plain text:

Antonio Lopez-Martinez-Carrasco, Jose M. Juarez, Manuel Campos, Francisco Mora-Caselles,
Subgroups: A Python library for Subgroup Discovery,
SoftwareX,
Volume 28,
2024,
101895,
ISSN 2352-7110,
https://doi.org/10.1016/j.softx.2024.101895.
(https://www.sciencedirect.com/science/article/pii/S2352711024002656)
Keywords: Machine learning; Data mining; Subgroup Discovery; Python