Scalable Approximate Gaussian Process using Local Kriging
Project description
Fast implementation of the MuyGPs Gaussian process hyperparameter estimation algorithm
MuyGPs is a GP estimation method that affords fast hyperparameter optimization by way of performing leave-one-out cross-validation. MuyGPs achieves best-in-class speed and scalability by limiting inference to the information contained in k nearest neighborhoods for prediction locations for both hyperparameter optimization and tuning. This feature affords the optimization of hyperparameters by way of leave-one-out cross-validation, as opposed to the more expensive loglikelihood evaluations required by similar sparse methods.
Installation
Pip installation instructions:
$ pip install muygpys
To install from source, follow these instructions:
$ git clone git@github.com:LLNL/MuyGPyS.git
$ pip install -e MuyGPyS
Additionally check out the develop branch to access the latest features in between stable releases.
Building Docs
Automatically-generated documentation can be found at readthedocs.io.
Doc building instructions:
$ cd /path/to/this/repo/docs
$ pip install -r requirements.txt
$ sphinx-build -b html docs docs/_build/html
Then open the file docs/_build/html/index.html
in your browser of choice.
Tutorials and Examples
Our documentation includes several jupyter notebook tutorials at
docs/examples
.
These tutorials are also include in the
online documentation.
See in particular the
univariate regression tutorial
for a low-level introduction to the use of MuyGPyS
.
See also the
regression api tutorial
describing how to coalesce the same simple workflow into a one-line call.
About
Authors
- Benjamin W. Priest (priest2 at llnl dot gov)
- Amanada L. Muyskens (muyskens1 at llnl dot gov)
Papers
MuyGPyS has been used the in the following papers (newest first):
- Gaussian Process Classification fo Galaxy Blend Identification in LSST
- Star-Galaxy Image Separation with Computationally Efficient Gaussian Process Classification
- Star-Galaxy Separation via Gaussian Processes with Model Reduction
Citation
If you use MuyGPyS in a research paper, please reference our article:
@article{muygps2021,
title={MuyGPs: Scalable Gaussian Process Hyperparameter Estimation Using Local Cross-Validation},
author={Muyskens, Amanda and Priest, Benjamin W. and Goumiri, Im{\`e}ne and Schneider, Michael},
journal={arXiv preprint arXiv:2104.14581},
year={2021}
}
License
MuyGPyS is distributed under the terms of the MIT license. All new contributions must be made under the MIT license.
See LICENSE-MIT, NOTICE, and COPYRIGHT for details.
SPDX-License-Identifier: MIT
Release
LLNL-CODE-824804
Project details
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