gpnam 0.0.24

GPNAM - an interpretable Gaussian Process Neural Additive Model.

GPNAM: Gaussian Process Neural Additive Models

The framework of GPNAM. $z_s, c_s$ and the sinusoidal function are predefined from the paper and do not require training. The only trainable parameter is W that maps to the output of each shape function.

This package implements the paper titled Gaussian Process Neural Additive Models that appears at AAAI 2024.

Basically, the GPNAM constructs a Neural Additive Model (NAM) by a GP with Random Fourier Features as the shape function for each input feature, which leads to a convex optimization with a significant reduction in trainable parameters.

Sklearn interface

You can install the package by:

pip install gpnam

Then, you can run the model simply by:

from gpnam.sklearn import GPNAM

"""
input_dim: the dimensions of input data
problem: type of the task, 'classification' or 'regression'
"""
gpnam = GPNAM(input_dim, problem)
gpnam.fit(X, y)

y_pred = gpnam.predict(X_test)

Citation

If you find this repo useful, please consider citing our paper:

@inproceedings{,
  title={Gaussian Process Neural Additive Models},
  author={Wei Zhang and Brian Barr and John Paisley},
  booktitle={AAAI Conference on Artificial Intelligence},
  year={2024}
}