Neural Additive Models (PyTorch)

This is a PyTorch re-implementation for neural additive models, check out:

• Neural Additive Models: Interpretable Machine Learning with Neural Nets.
• TensorFlow OG Implementation

Install Package

pip install nam-pt

Dependencies

• torch==1.7.0
• fsspec==0.8.4
• pandas==1.1.4
• tqdm==4.54.0
• sklearn==0.0
• absl-py==0.11.0
• gcsfs==0.7.1

Usage

conda env create -f environment.yml
conda activate nam-pt
python run.py

In Python:

from nam import NeuralAdditiveModel

model = NeuralAdditiveModel(input_size=x_train.shape[-1],
                            shallow_units=100,
                            hidden_units=(64, 32, 32),
                            shallow_layer=ExULayer,
                            hidden_layer=ReLULayer,
                            hidden_dropout=0.1,
                            feature_dropout=0.1)
logits, feature_nn_outputs = model.forward(x)

Citing

If you use this code in your research, please cite the following paper:

Agarwal, R., Frosst, N., Zhang, X., Caruana, R., & Hinton, G. E. (2020). Neural additive models: Interpretable machine learning with neural nets. arXiv preprint arXiv:2004.13912

  @article{agarwal2020neural,
    title={Neural additive models: Interpretable machine learning with neural nets},
    author={Agarwal, Rishabh and Frosst, Nicholas and Zhang, Xuezhou and
    Caruana, Rich and Hinton, Geoffrey E},
    journal={arXiv preprint arXiv:2004.13912},
    year={2020}
  }

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Disclaimer about COMPAS dataset: It is important to note that developing a machine learning model to predict pre-trial detention has a number of important ethical considerations. You can learn more about these issues in the Partnership on AI Report on Algorithmic Risk Assessment Tools in the U.S. Criminal Justice System. The Partnership on AI is a multi-stakeholder organization -- of which Google is a member -- that creates guidelines around AI.

We’re using the COMPAS dataset only as an example of how to identify and remediate fairness concerns in data. This dataset is canonical in the algorithmic fairness literature.

Disclaimer: This is not an official Google product.