https://github.com/EmuKit/emukit

A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
https://github.com/EmuKit/emukit

bayesian-optimization bayesian-quadrature decision-making emulation experimental-design machine-learning multi-fidelity python sensitivity-analysis uncertainty-quantification

Last synced: 2 days ago
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A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.

• Host: GitHub
• URL: https://github.com/EmuKit/emukit
• Owner: EmuKit
• License: apache-2.0
• Created: 2018-09-04T05:01:34.000Z (about 6 years ago)
• Default Branch: main
• Last Pushed: 2024-05-07T13:49:56.000Z (6 months ago)
• Last Synced: 2024-05-07T14:56:10.438Z (6 months ago)
• Topics: bayesian-optimization, bayesian-quadrature, decision-making, emulation, experimental-design, machine-learning, multi-fidelity, python, sensitivity-analysis, uncertainty-quantification
• Language: Python
• Homepage: https://emukit.github.io/emukit/
• Size: 15.6 MB
• Stars: 565
• Watchers: 17
• Forks: 127
• Open Issues: 38
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

        
# Emukit

[![Build Status](https://github.com/EmuKit/emukit/workflows/Tests/badge.svg)](https://github.com/EmuKit/emukit/actions?query=workflow%3ATests) |

[![Documentation Status](https://readthedocs.org/projects/emukit/badge/?version=latest)](https://emukit.readthedocs.io/en/latest/?badge=latest) |

[![Tests Coverage](https://codecov.io/gh/emukit/emukit/branch/main/graph/badge.svg)](https://codecov.io/gh/emukit/emukit) |

[![GitHub License](https://img.shields.io/github/license/emukit/emukit.svg)](https://github.com/emukit/emukit/blob/main/LICENSE)

[Website](https://emukit.github.io/) |

[Documentation](https://emukit.readthedocs.io/) |

[Contribution Guide](CONTRIBUTING.md)

Emukit is a highly adaptable Python toolkit for enriching decision making under uncertainty. This is particularly pertinent to complex systems where data is scarce or difficult to acquire. In these scenarios, propagating well-calibrated uncertainty estimates within a design loop or computational pipeline ensures that constrained resources are used effectively.

The main features currently available in Emukit are:

* **Multi-fidelity emulation:** build surrogate models when data is obtained from multiple information sources that have different fidelity and/or cost;

* **Bayesian optimisation:** optimise physical experiments and tune parameters of machine learning algorithms;

* **Experimental design/Active learning:** design the most informative experiments and perform active learning with machine learning models;

* **Sensitivity analysis:** analyse the influence of inputs on the outputs of a given system;

* **Bayesian quadrature:** efficiently compute the integrals of functions that are expensive to evaluate.

Emukit is agnostic to the underlying modelling framework, which means you can use any tool of your choice in the Python ecosystem to build the machine learning model, and still be able to use Emukit.

## Installation

To install emukit, simply run

```

pip install emukit

```

For other install options, see our [documentation](https://emukit.readthedocs.io/en/latest/installation.html).

### Dependencies / Prerequisites

Emukit's primary dependencies are Numpy and GPy.

See [requirements](requirements/requirements.txt).

## Getting started

For examples see our [tutorial notebooks](http://nbviewer.jupyter.org/github/emukit/emukit/blob/main/notebooks/index.ipynb).

## Documentation

To learn more about Emukit, refer to our [documentation](https://emukit.readthedocs.io).

To learn about emulation as a concept, check out the [Emukit playground](https://github.com/amzn/Emukit-playground) project.

## Citing the library

If you are using emukit, we would appreciate if you could cite our papers about Emukit in your research:

    @inproceedings{emukit2019,

      author = {Paleyes, Andrei and Pullin, Mark and Mahsereci, Maren and McCollum, Cliff and Lawrence, Neil and González, Javier},

      title = {Emulation of physical processes with {E}mukit},

      booktitle = {Second Workshop on Machine Learning and the Physical Sciences, NeurIPS},

      year = {2019}

    }

    @article{emukit2023,

      title={Emukit: A {P}ython toolkit for decision making under uncertainty},

      author={Andrei Paleyes and Maren Mahsereci and Neil D. Lawrence},

      journal={Proceedings of the Python in Science Conference},

      year={2023}

    }

The papers themselves can be found at these links: [NeurIPS workshop 2019](https://arxiv.org/abs/2110.13293), [SciPy conference 2023](https://conference.scipy.org/proceedings/scipy2023/emukit.html).

## License

Emukit is licensed under Apache 2.0. Please refer to [LICENSE](LICENSE) and [NOTICE](NOTICE) for further license information.