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Deep Markov Gaussian Processes (DMGP)

python pytorch version Documentation Status License: MIT

DMGP is a Python library for sparse deep Gaussian processes (DGPs) and uncertainty estimation with GPU acceleration. It is built on top of PyTorch and provides a simple and flexible API for building complex deep GP models as trainable neural networks.

Tutorials and Documentation

See our documentation, examples, tutorials on how to construct all sorts of DGP models in DMGP.

Installation

Requirements:

  • Python >= 3.8
  • PyTorch >= 1.9.0
  • SciPy >= 1.7.0

To install core library using pip:

pip install -i https://test.pypi.org/simple/ dmgp

To install latest development version from source:

git clone https://github.com/warrenzha/dmgp.git
cd dmgp
pip install -r requirements.txt

Usage

There are two ways to build sparse DGPs using DMGP:

  1. Load a pre-trained model from the library
  2. Define your custom model with modules provided in the library

Load a pre-trained model

$ cd examples
$ python bayesian_mnist.py --model [additive]
                           --mode [test]
                           --batch-size [batch_size]
                           --epochs [epochs]
                           --lr [learning_rate]
                           --save_dir [save_directory] 
                           --num_monte_carlo [num_monte_carlo_inference]
                           --num_mc [num_monte_carlo_training]
                           --log_dir [logs_directory]

Define your custom model

import torch
from dmgp.layers import LinearFlipout
from dmgp.kernels import LaplaceProductKernel
from dmgp.utils.sparse_design import HyperbolicCrossDesign
from dmgp.models import DMGP

batch, dim = 1000, 7
x = torch.randn(batch, dim).to("cuda")
model = DMGP(
    input_dim = dim,
    output_dim = 1,
    num_layers = 2,
    num_inducing = 2,
    hidden_dim = 8,
    kernel = LaplaceProductKernel(lengthscale=1.),
    design_class = HyperbolicCrossDesign,
    layer_type = LinearFlipout,
    option = 'additive',
).to("cuda")
y = model(x)

Citing Us

If you use DMGP, please cite as:

@software{zhao2024dgpsparse,
  author       = {Wenyuan Zhao and Haoyuan Chen},               
  title        = {DMGP: Sparse expansion for deep Gaussian processes in PyTorch},
  month        = jul,
  year         = 2024,
  doi          = {},
  url          = {https://dmgp.readthedocs.io/},
  howpublished = {\url{https://github.com/warrenzha/dmgp.git}}
}

A Sparse Expansion for Deep Gaussian Processes

@article{ding2024sparse,
  title={A sparse expansion for deep Gaussian processes},
  author={Ding, Liang and Tuo, Rui and Shahrampour, Shahin},
  journal={IISE Transactions},
  volume={56},
  number={5},
  pages={559--572},
  year={2024},
  publisher={Taylor \& Francis}
}

Some BNN implementation is based on Bayesian-Torch

@software{krishnan2022bayesiantorch,
  author       = {Ranganath Krishnan and Pi Esposito and Mahesh Subedar},               
  title        = {Bayesian-Torch: Bayesian neural network layers for uncertainty estimation},
  month        = jan,
  year         = 2022,
  doi          = {10.5281/zenodo.5908307},
  url          = {https://doi.org/10.5281/zenodo.5908307}
  howpublished = {\url{https://github.com/IntelLabs/bayesian-torch}}
}