mogp-emulator 0.6.0

Tool for Multi-Output Gaussian Process Emulators

Multi-Output Gaussian Process Emulator

mogp_emulator is a Python package for fitting Gaussian Process Emulators to computer simulation results. The code contains routines for fitting GP emulators to simulation results with a single or multiple target values, optimizing hyperparameter values, and making predictions on unseen data. The library also implements experimental design, dimension reduction, and calibration tools to enable modellers to understand complex computer simulations.

Installation

mogp_emulator requires Python version 3.6 or later. The code and all of its dependencies can be installed via pip:

pip install mogp-emulator

Optionally, you may want to install some additional optional packages. matplotlib is useful for visualising some of the results of the benchmarks, and patsy is highly recommended for users that wish to parse R-style string formulas for specifying mean functions. These can be found in the requirements-optional.txt file in the main repository.

Documentation

The documentation is available at readthedocs for the current builds of the master and devel versions, plus any previous tagged releases. The documentation is available there in HTML, PDF, or e-Pub format.

Getting Help

This package is under active development by the Research Engineering Group at the Alan Turing Institute as part of several projects on Uncertainty Quantification. Questions about the code or any feedback on the usability and features that you would find useful can be sent to Eric Daub <[e-mail]>. If you encounter any bugs or problems with installing the software, please see the Issues tab on the Github page, and if the issue is not present, create a new one.

Contributing

If you find this software useful, please consider taking part in its development! We aim to make this a welcoming, collaborative, open-source project where users of any background or skill levels feel that they can make valuable contributions. Please see the Code of Conduct for more on our expectations for participation in this project, and our Contributing Guidelines for how to get involved.