https://github.com/jungtaekkim/bayeso-benchmarks

Benchmark functions for Bayesian optimization
https://github.com/jungtaekkim/bayeso-benchmarks

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Benchmark functions for Bayesian optimization

• Host: GitHub
• URL: https://github.com/jungtaekkim/bayeso-benchmarks
• Owner: jungtaekkim
• License: mit
• Created: 2019-12-17T08:04:29.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2024-03-12T20:14:45.000Z (over 1 year ago)
• Last Synced: 2025-04-12T05:53:38.043Z (2 months ago)
• Language: Python
• Homepage: https://bayeso.org
• Size: 146 MB
• Stars: 33
• Watchers: 2
• Forks: 6
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-offline-model-based-optimization - **BayesO Benchmarks: Benchmark Functions for Bayesian Optimization** - benchmarks) [📖](https://github.com/mila-iqia/Awesome-Offline-Model-Based-Optimization/blob/main/bibtex/bayeso.txt) (🔗 Benchmark / Task)
• awesome-offline-model-based-optimization - **BayesO Benchmarks: Benchmark Functions for Bayesian Optimization** - benchmarks) [📖](https://github.com/mila-iqia/Awesome-Offline-Model-Based-Optimization/blob/main/bibtex/bayeso.txt) (🔗 Benchmark / Task)

README

        






# BayesO Benchmarks: Benchmark Functions for Bayesian Optimization

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7577330.svg)](https://doi.org/10.5281/zenodo.7577330)

[![Build Status](https://github.com/jungtaekkim/bayeso-benchmarks/actions/workflows/pytest.yml/badge.svg)](https://github.com/jungtaekkim/bayeso-benchmarks/actions/workflows/pytest.yml)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

This repository provides the implementation of benchmark functions for Bayesian optimization.

The details of benchmark functions can be found in [these notes](https://jungtaek.github.io/notes/benchmarks_bo.pdf).

* [https://bayeso.org](https://bayeso.org)

## Installation

We recommend installing it with `virtualenv`.

You can choose one of three installation options.

* Using PyPI repository (for user installation)

To install the released version in PyPI repository, command it.

```shell

pip install bayeso-benchmarks

```

* Using source code (for developer installation)

To install `bayeso-benchmarks` from source code, command the following in the `bayeso-benchmarks` root.

```shell

pip install .

```

* Using source code (for editable development mode)

To use editable development mode, command the following in the `bayeso-benchmarks` root.

```shell

pip install -e .

```

If you want to install the packages required for development, you can simply add `[dev]`.

For example, `pip install .[dev]` or `pip install -e .[dev]`.

* Uninstallation

If you would like to uninstall `bayeso-benchmarks`, command it.

```shell

pip uninstall bayeso-benchmarks

```

## Simple Example

A simple example on Branin function is shown below.

```python

from bayeso_benchmarks import Branin

obj_fun = Branin()

bounds = obj_fun.get_bounds()

X = obj_fun.sample_uniform(100)

Y = obj_fun.output(X)

Y_noise = obj_fun.output_gaussian_noise(X)

```

## Citation

```

@misc{KimJ2023software,

    author={Kim, Jungtaek},

    title={{BayesO Benchmarks}: Benchmark Functions for {Bayesian} Optimization},

    doi={10.5281/zenodo.7577330},

    url={https://github.com/jungtaekkim/bayeso-benchmarks},

    howpublished={\url{https://doi.org/10.5281/zenodo.7577330}},

    year={2023}

}

```

## License

[MIT License](LICENSE)