This repo is a simplified version designed to use the GAM models with your own datasets. In this repo, we provide the following GAMs implementation in Python:
- XGB (xgboost with depth 1 and visualized as a GAM)
- EBM (Explainable boosting machine https://github.com/interpretml/interpret)
- Spline (both pygam package and R mgcv package)
- Fused Lasso Additive Model (FLAM)
- Logistic Regression
To fully reproduce the paper's result, please see the repo https://github.com/zzzace2000/GAMs
We use Python 3.6 and the following packages:
pip install pandas scikit-learn numpy seaborn xgboost interpret rpy2 pygam
We also use the R packages to run the R spline and FLAM models. To install:
- If you already have R installed, go into the R console and run
install.packages('mgcv')
install.packages('flam')
- If you do not have R installed and are using conda environment, you can do:
conda install -c r r r-mgcv
Then install flam inside R console (run R()):
install.packages('flam')
The main usages are similar to scikit-learn models:
from arch import MyXGBClassifier, MyBaggingClassifier
xgb = MyXGBClassifier(max_depth=1)
# And do a bagging 20 times
xgb = MyBaggingClassifier(base_estimator=xgb, n_estimators=20)
xgb.fit(X, y)
df = xgb.get_GAM_plot_dataframe()
df.head()
The dataframe would have all the shape plots details and feature importances. Then you can visualize by:
from vis_utils import vis_main_effects
fig, axes = vis_main_effects({
'XGB': xgb.get_GAM_plot_dataframe(),
})
See the main.ipynb for how to use.
If you find the code useful, please cite:
@article{chang2020interpretable,
title={How Interpretable and Trustworthy are GAMs?},
author={Chang, Chun-Hao and Tan, Sarah and Lengerich, Ben and Goldenberg, Anna and Caruana, Rich},
journal={arXiv preprint arXiv:2006.06466},
year={2020}
}