subgroups is a python library which contains a collection of subgroup discovery algorithms and other data analysis utilities.
Project description
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:
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 sphinx==8.1.3
python -m pip install sphinx-rtd-theme==3.0.2
python -m pip install sphinx-autodoc-typehints==2.5.0
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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file subgroups-0.1.11.tar.gz.
File metadata
- Download URL: subgroups-0.1.11.tar.gz
- Upload date:
- Size: 199.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.0.1 CPython/3.13.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
d1dc3986c3eacb430b28b44580b9c245c9bf0c2550ca0360fd2445cfca8475b1
|
|
| MD5 |
ba923032d8d85d1c204fcfe2a88f9674
|
|
| BLAKE2b-256 |
935fda8c70e14ca75ad38e8ad2c0f981cc257b67f13a3b6ef805513f4387f83a
|
File details
Details for the file subgroups-0.1.11-py3-none-any.whl.
File metadata
- Download URL: subgroups-0.1.11-py3-none-any.whl
- Upload date:
- Size: 282.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.0.1 CPython/3.13.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
ad4caecf0f018506c0c36ffd5d51081dc1c00b15d0f45021e4a1b7577321042f
|
|
| MD5 |
5d3eaed09427b3af723bfe53ff52e9ec
|
|
| BLAKE2b-256 |
6166890068c5c3683419b6dc9c043baaca37530d42e6e3460746512902be9012
|