jobregon1212 / rulecosi Goto Github PK

RuleCOSI is a machine learning package that combine and simplifies tree ensembles and generates a single rule-based classifier that is smaller and simpler. It was developed in the Industrial Artificial Intelligence Laboratory (IAI) at Kyung Hee University by (Josue Obregon). The implementation is compatible with scikit-learn.

rulecosi is tested to work under Python 3.9+. The dependency requirements used when developing the library are:

• numpy>=1.22.3
• scipy>=1.8.0
• scikit-learn>=1.0.2
• gmpy2>=2.1.2
• pandas>=1.4.1
• bitarray>=2.5.1
• xgboost>=1.5.2 (optional)
• lightgbm>=3.3.2 (optional)
• catboost>=1.0.4 (optional)

Right now it is just available from GitHub. You can clone it and run the setup.py file. Use the following commands to get a copy from Github and install all basic dependencies:

git clone https://github.com/jobregon1212/rulecosi.git
cd rulecosi
pip install .

This installs the basic rulecosi package. It will only work with the following scikit-learn tree ensembles: BaggingClassifier, RandomForestClassifier and GradientBoostingClassifier.

If you want to install the package with support to other ensembles, you have to add the required packages separated by commas inside square brackets when you install rulecosi. For example if you would like to have XGBoost support you have to run the following command:

git clone https://github.com/jobregon1212/rulecosi.git
cd rulecosi
pip install .[xgboost]

The supported optional packages are xgboost, lightgbm and catboost.

The python documentation is available in this link.

The development of rulecosi tried to be in line with the one of the scikit-learn community. Therefore, you can refer to their Development Guide.

If you use rulecosi in a scientific publication, we would appreciate citations to the following paper:

@article{obregon2022rulecosi+,
title = {RuleCOSI+: Rule extraction for interpreting classification tree ensembles},
journal = {Information Fusion},
volume = {89},
pages = {355-381},
year = {2023},
issn = {1566-2535},
doi = {https://doi.org/10.1016/j.inffus.2022.08.021},
url = {https://www.sciencedirect.com/science/article/pii/S1566253522001129},
author = {Josue Obregon and Jae-Yoon Jung}

}

The algorithm works with different type of ensembles and it uses the implementations provided by the sklearn package. The supported tree ensemble types are:

1. BaggingClassifier
2. RandomForestClassifier
3. GradientBoostingClassifier
4. XGBClassifier
5. LGBMClassifier
6. CatBoostClassifier

For more information you can check the usage in the docstrings or the examples folder of this repository.