woodtapper 0.1.0

User-friendly Python toolbox for interpreting and manipulating decision tree ensembles from scikit-learn

🪵 Key Features

WoodTapper is a Python toolbox that provides:

• Rule extraction from tree-based ensembles: Generates a final estimator composed of a sequence of simple rule-based on features and thresholds.

• Fairness-Constrained Gradient Boosting: A second-order tree-boosting implementation that directly incorporates demographic fairness into the training objective via a Wasserstein distance penalty.

• Example-based explanations: Connects predictions to a small set of representative samples, returning the most similar examples along with their target values.

Detailed information about the modules can be found here.

WoodTapper is supported by a peer reviewed publication:

    WoodTapper: a Python package for explaining decision tree ensembles, Sakho et al. (2026) 📄

and a preprint:

    OT-FairBoost: Optimal Transport-Guided Gradient Boosting for Fairness Regularization on Tabular Data, Shivola et al. (2026) 📄

🛠 Installation

From PyPi:

pip install woodtapper

Compatibility note: WoodTapper includes a compiled Cython component that depends on the binary interface of scikit-learn. If scikit-learn is already installed in your environment, the prebuilt PyPI wheel may be incompatible with it. WoodTapper is currently pinned to scikit-learn 1.6.1: versions 1.9 and newer are not compatible with this component. To use another compatible version, rebuild WoodTapper locally with:

pip uninstall -y woodtapper
pip install -U pip setuptools wheel
pip install -U Cython pybind11
pip install --no-binary=woodtapper --no-build-isolation woodtapper

From source:

git clone https://github.com/artefactory/woodtapper.git
cd woodtapper
pip install -e .[dev,docs]

Warning: If you are a Windows user, you need to have a C/C++ compiler before installing woodtapper.

🌿 WoodTapper RulesExtraction module

from woodtapper.extract_rules import SirusClassifier
from woodtapper.extract_rules.visualization import show_rules

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)
show_rules(sirus, max_rules=10)

⚖️ WoodTapper boosted trees Fairness module

from woodtapper.fairness import OTFairBoostClassifier

otfair_clf = OTFairBoostClassifier(
    n_estimators=100,
    lambda_fairness_value=0.1,
    fairness_mode="Demographic_Parity",
    verbosity=-1,
)

otfair_clf.fit(X_train, y_train,sensitive_attribute_train)
otfair_clf.predict(X_test)

🌱 WoodTapper ExampleExplanation module

from woodtapper.example_sampling import RandomForestClassifierExplained

rf_explained = RandomForestClassifierExplained(n_estimators=100)
rf_explained.fit(X_train, y_train)

# Get the 5 most similar samples (and target) for each test sample
Xy_explain = rf_explained.explanation(X_test)

🙏 Acknowledgements

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

📜 Citation

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

@article{Sakho2026,
doi = {10.21105/joss.10112},
url = {https://doi.org/10.21105/joss.10112},
year = {2026}, publisher = {The Open Journal},
volume = {11},
number = {121},
pages = {10112},
author = {Sakho, Abdoulaye and Aouad, Jad and Gauthier, Carl-Erik and Malherbe, Emmanuel and Scornet, Erwan},
title = {WoodTapper: a Python package for explaining decision tree ensembles},
journal = {Journal of Open Source Software} }

@article{shilova2026ot,
  title={OT-FairBoost: Optimal Transport-Guided Gradient Boosting for Fairness Regularization on Tabular Data},
  author={Shilova, Veronika and Sakho, Abdoulaye and Boumoussou, Younes and Risser, Laurent and Loubes, Jean-Michel and Malherbe, Emmanuel},
  journal={arXiv preprint arXiv:2607.28014},
  year={2026}
}

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}
}