woodtapper 0.0.10

A Python toolbox for interpretable and explainable tree ensembles.

User-friendly and scalable Python package for tapping decision tree ensembles

WoodTapper is a Python toolbox for interpretable and explainable tree ensembles learning, fully compatible with the scikit-learn API.

🪵 Key Features

• Rule extraction from tree-based ensembles.
• Example-based explanation module that links predictions to a small set of representative samples.

🛠 Installation

From PyPi:

pip install woodtapper

🌿 WoodTapper RulesExtraction module

## RandomForestClassifier rules extraction
from woodtapper.extract_rules import SirusClassifier

SIRUS = SirusClassifier(n_estimators=1000,max_depth=2,
                          quantile=10,p0=0.01, random_state=0)
SIRUS.fit(X_train,y_train)
y_pred_sirus = SIRUS.predict(X_test)

🌱 WoodTapper ExampleExplanation module

## RandomForestClassifier rules extraction
from woodtapper.example_sampling import RandomForestClassifierExplained

RFExplained = RandomForestClassifierExplained(n_estimators=100)
RFExplained.fit(X_train,y_train)
example_explanation = RFExplained.explanation(X_test) # Get the 5 most similar samples for each test sample

🙏 Acknowledgements

This work was done through a partenership between Artefact Research Center and the Laboratoire de Probabilités Statistiques et Modélisation (LPSM) of Sorbonne University.

   

📜 Citation

If you find the code usefull, please consider citing us :

@misc{woodtapper,
  title        = {WoodTapper: a Python package for tapping decision tree ensembles},
  author       = {Sakho, Abdoulaye and AOUAD, Jad and Malherbe, Emmanuel and Scornet, Erwan},
  year         = {2025},
  howpublished = {\url{https://github.com/artefactory/woodtapper}},
}

For SIRUS methodology, consider citing:

@article{benard2021sirus,
  title={Sirus: Stable and interpretable rule set for classification},
  author={Benard, Clement and Biau, Gerard and Da Veiga, Sebastien and Scornet, Erwan},
  year={2021}
}