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Description:

qshap 0.3.2

Q-SHAP: Feature-Specific R2 Values for Tree Ensembles

This package is used to compute feature-specific R2 values, following Shapley decomposition of the total R2, for tree ensembles in polynomial time based on the paper.
This version only takes outputs from XGBoost, scikit-learn Decision Tree, and scikit-learn GBDT. We are working to update it for random forests in the next version. Please check Q-SHAP Tutorial.ipynb for more details using Q-SHAP.
Installation
qshap can be installed through PyPI:
pip install qshap

Quick Start
# Import necessary libraries
from sklearn.datasets import fetch_california_housing
from qshap import gazer, vis
import xgboost as xgb
import numpy as np

# Load the California Housing dataset and fit a XGBoost regressor
housing = fetch_california_housing()
x, y, feature_names = housing.data, housing.target, housing.feature_names
model = xgb.XGBRegressor(max_depth=2, n_estimators=50, random_state=42).fit(x, y)

# Obtain feature-specific R^2 using qshap, use 1024 randomly sampled data
gazer_rsq = gazer(model)
phi_rsq = gazer.rsq(gazer_rsq, x, y, nsample=1024, random_state=42)

# Visualize top values of feature-specific R^2
vis.rsq(phi_rsq, label=np.array(feature_names), rotation=30, save_name="cal_housing", color_map_name="Pastel2")

Citation
@article{jiang2024feature,
title={Feature-Specific Coefficients of Determination in Tree Ensembles},
author={Jiang, Zhongli and Zhang, Dabao and Zhang, Min},
journal={arXiv preprint arXiv:2407.03515},
year={2024}
}

References

Jiang, Z., Zhang, D., & Zhang, M. (2024). "Feature-specific coefficients of determination in tree ensembles." arXiv preprint arXiv:2407.03515.
Lundberg, Scott M., et al. "From local explanations to global understanding with explainable AI for trees." Nature Machine Intelligence 2.1 (2020): 56-67.
Karczmarz, Adam, et al. "Improved feature importance computation for tree models based on the Banzhaf value." Uncertainty in Artificial Intelligence. PMLR, 2022.
Bifet, Albert, Jesse Read, and Chao Xu. "Linear tree shap." Advances in Neural Information Processing Systems 35 (2022): 25818-25828.
Chen, Tianqi, and Carlos Guestrin. "Xgboost: A scalable tree boosting system." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016.

Container Images
We provide pre-built images, available for both Docker and Singularity, with all necessary packages for Q-SHAP in Python 3.12:

Docker:
You can pull the Docker image using the following command:
docker pull catstat/xai

Singularity:
You can pull the Docker image using the following command:
singularity pull docker://catstat/xai:0.1

Task List

Task 1: Lightgbm version
Task 2: Catboost version