https://github.com/cog-imperial/entmoot

Multiobjective black-box optimization using gradient-boosted trees
https://github.com/cog-imperial/entmoot

bayesian-optimization black-box-optimization gradient-boosted-trees hyperparameter-optimization mixed-integer-programming multiobjective-optimization optimization python

Last synced: 3 days ago
JSON representation

Multiobjective black-box optimization using gradient-boosted trees

• Host: GitHub
• URL: https://github.com/cog-imperial/entmoot
• Owner: cog-imperial
• License: bsd-3-clause
• Created: 2020-08-26T16:48:31.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2026-02-19T13:43:04.000Z (about 2 months ago)
• Last Synced: 2026-02-19T17:30:05.520Z (about 2 months ago)
• Topics: bayesian-optimization, black-box-optimization, gradient-boosted-trees, hyperparameter-optimization, mixed-integer-programming, multiobjective-optimization, optimization, python
• Language: Python
• Homepage: https://entmoot.readthedocs.io/
• Size: 997 KB
• Stars: 62
• Watchers: 4
• Forks: 12
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

Awesome Lists containing this project

README

          
[![Tests](https://github.com/cog-imperial/entmoot/actions/workflows/python-package.yml/badge.svg?branch=entmoot-v2)](https://github.com/cog-imperial/entmoot/actions/workflows/python-package.yml)

[![Read the Docs](https://readthedocs.org/projects/entmoot/badge/?version=master)](https://entmoot.readthedocs.io/en/master)



`ENTMOOT` (**EN**semble **T**ree **MO**del **O**ptimization **T**ool) is a framework to perform Bayesian Optimization using tree-based surrogate models. Gradient-boosted tree models from `lightgbm` are combined with a distance-based uncertainty 

measure in a deterministic global optimization framework to optimize black-box functions. More

details on the method here: https://arxiv.org/abs/2003.04774.

## Documentation

The docs can be found here: https://entmoot.readthedocs.io/

## How to reference ENTMOOT

When using any `ENTMOOT` for any publications please reference this software package as:

```

@article{thebelt2021entmoot,

  title={ENTMOOT: A framework for optimization over ensemble tree models},

  author={Thebelt, Alexander and Kronqvist, Jan and Mistry, Miten and Lee, Robert M and Sudermann-Merx, Nathan and Misener, Ruth},

  journal={Computers \& Chemical Engineering},

  volume={151},

  pages={107343},

  year={2021},

  publisher={Elsevier}

}

```

## Authors

* **[Alexander Thebelt](https://optimisation.doc.ic.ac.uk/person/alexander-thebelt/)** ([ThebTron](https://github.com/ThebTron)) - Imperial College London

* **[Nathan Sudermann-Merx](https://www.mannheim.dhbw.de/profile/sudermann-merx)** ([spiralulam](https://github.com/spiralulam)) - Cooperative State University Mannheim

* **Toby Boyne** ([ThebTron](https://github.com/TobyBoyne)) - Imperial College London

## License

The ENTMOOT package is released under the BSD 3-Clause License. Please refer to the [LICENSE](https://github.com/cog-imperial/entmoot/blob/master/LICENSE) file for details.

## Acknowledgements

The support of BASF SE, Lugwigshafen am Rhein is gratefully acknowledged.