muygpys 0.4.1

Scalable Approximate Gaussian Process using Local Kriging

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 [e-mail]: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):

1. Gaussian Process Classification fo Galaxy Blend Identification in LSST
2. Star-Galaxy Image Separation with Computationally Efficient Gaussian Process Classification
3. 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