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:

1. XGB (xgboost with depth 1 and visualized as a GAM)
2. EBM (Explainable boosting machine https://github.com/interpretml/interpret)
3. Spline (both pygam package and R mgcv package)
4. Fused Lasso Additive Model (FLAM)
5. 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:

1. If you already have R installed, go into the R console and run

install.packages('mgcv')
install.packages('flam')

2. 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}
}