rulelist 0.2.0

Learn rule lists from data for classification, regression or subgroup discovery

MDL Rule Lists for prediction and subgroup discovery.

This repository contains the code for using rule lists for univariate or multivariate classification or regression and its equivalents in Data Mining and Subgroup Discovery. These models use the Minimum Description Length (MDL) principle as optimality criteria.

Dependencies

This project was written for Python 3.7. All required packages from PyPI are specified in the requirements.txt.

NOTE: This list of packages includes the gmpy2 package.

Installation

The latest release can be installed using pip:

pip install rulelist

If you run into issues regarding the gmpy2 package mentioned above, please refer to their documentation for help.

For the current version, you can clone the repository and install the dependencies locally:

git clone https://github.com/HMProenca/RuleList.git
cd RuleList
pip install -r requirements.txt

Example of usage for prediction:

import pandas as pd
from rulelist import RuleList
from sklearn import datasets
from sklearn.model_selection import train_test_split

task = 'prediction'
target_model = 'categorical'

data = datasets.load_breast_cancer()
Y = pd.Series(data.target)
X = pd.DataFrame(data.data)

X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.3)

model = RuleList(task = task, target_model = target_model)

model.fit(X_train, y_train)

y_pred = model.predict(X_test)
from sklearn.metrics import accuracy_score
accuracy_score(y_test.values,y_pred)

print(model)

Example of usage for subgroup discovery:

import pandas as pd
from rulelist.rulelist import RuleList
from sklearn import datasets

task = 'discovery'
target_model = 'gaussian'

data = datasets.load_boston()
y = pd.Series(data.target)
X = pd.DataFrame(data.data)

model = RuleList(task = task, target_model = target_model)

model.fit(X, y)

print(model)

Contact

If there are any questions or issues, please contact me by mail at [e-mail] or open an issue here on Github.

Citation

In a machine learning (prediction) context for problems of classification, regression, multi-label classification, multi-category classification, or multivariate regression cite the corresponding bibtex of the first classification application of MDL rule lists:

@article{proencca2020interpretable,
  title={Interpretable multiclass classification by MDL-based rule lists},
  author={Proen{\c{c}}a, Hugo M and van Leeuwen, Matthijs},
  journal={Information Sciences},
  volume={512},
  pages={1372--1393},
  year={2020},
  publisher={Elsevier}
}

in the context of data mining and subgroup discovery please refer to subgroup lists:

@article{proencca2020discovering,
  title={Discovering outstanding subgroup lists for numeric targets using MDL},
  author={Proen{\c{c}}a, Hugo M and Gr{\"u}nwald, Peter and B{\"a}ck, Thomas and van Leeuwen, Matthijs},
  journal={arXiv preprint arXiv:2006.09186},
  year={2020}
} 

and

@article{proencca2021robust,
  title={Robust subgroup discovery},
  author={Proen{\c{c}}a, Hugo Manuel and B{\"a}ck, Thomas and van Leeuwen, Matthijs},
  journal={arXiv preprint arXiv:2103.13686},
  year={2021}
}

References

• Interpretable multiclass classification by MDL-based rule lists. Hugo M. Proença, Matthijs van Leeuwen. Information Sciences 512 (2020): 1372-1393. or publicly available in ArXiv -- experiments code (old version) available here
• Discovering outstanding subgroup lists for numeric targets using MDL. Hugo M. Proença, Peter Grünwald, Thomas Bäck, Matthijs van Leeuwen. ECML-PKDD(2020): -- experiments code available here
• Robust subgroup discovery. Hugo M. Proença,Thomas Bäck, Matthijs van Leeuwen. (2021) -- experiments code available here