Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/issp-center-dev/PHYSBO
PHYSBO -- optimization tools for PHYsics based on Bayesian Optimization
https://github.com/issp-center-dev/PHYSBO
bayesian-optimization
Last synced: about 2 months ago
JSON representation
PHYSBO -- optimization tools for PHYsics based on Bayesian Optimization
- Host: GitHub
- URL: https://github.com/issp-center-dev/PHYSBO
- Owner: issp-center-dev
- Created: 2020-11-17T06:59:25.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2024-08-30T08:38:57.000Z (4 months ago)
- Last Synced: 2024-09-21T13:49:05.891Z (3 months ago)
- Topics: bayesian-optimization
- Language: Python
- Homepage: https://www.pasums.issp.u-tokyo.ac.jp/physbo/en
- Size: 15.3 MB
- Stars: 68
- Watchers: 7
- Forks: 15
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-self-driving-labs - PHYSBO
README
# optimization tools for PHYsics based on Bayesian Optimization ( PHYSBO )
Bayesian optimization has been proven as an effective tool in accelerating scientific discovery.
A standard implementation (e.g., scikit-learn), however, can accommodate only small training data.
PHYSBO is highly scalable due to an efficient protocol that employs Thompson sampling, random feature maps, one-rank Cholesky update and automatic hyperparameter tuning. Technical features are described in [COMBO's document](https://github.com/tsudalab/combo/blob/master/docs/combo_document.pdf) and [PHYSBO's report](https://doi.org/10.1016/j.cpc.2022.108405) (open access).
PHYSBO was developed based on [COMBO](https://github.com/tsudalab/combo) for academic use.
## Document
- Stable (master branch)
- [English](https://issp-center-dev.github.io/PHYSBO/manual/master/en/index.html)
- [日本語](https://issp-center-dev.github.io/PHYSBO/manual/master/ja/index.html)
- Latest (develop branch)
- [English](https://issp-center-dev.github.io/PHYSBO/manual/develop/en/index.html)
- [日本語](https://issp-center-dev.github.io/PHYSBO/manual/develop/ja/index.html)
## Required Packages
- Python >= 3.6
- No longer tested with Python 3.6
- NumPy < 2.0.0
- SciPy
## Install
- From PyPI (recommended)
```bash
python3 -m pip install physbo
```
- From source (for developers)
1. Update pip (>= 19.0)
```bash
python3 -m pip install -U pip
```
1. Download or clone the github repository
```
git clone https://github.com/issp-center-dev/PHYSBO
```
1. Install via pip
``` bash
# ./PHYSBO is the root directory of PHYSBO
# pip install options such as --user are avaiable
python3 -m pip install ./PHYSBO
```
1. Note: Do not `import physbo` at the root directory of the repository because `import physbo` does not try to import the installed PHYSBO but one in the repository, which includes Cython codes not compiled.
## Uninstall
```bash
python3 -m pip uninstall physbo
```
## Usage
['examples/simple.py'](https://github.com/issp-center-dev/PHYSBO/examples/simple.py) is a simple example.
## Data repository
A tutorial and a dataset of a paper about PHYSBO can be found in [PHYSBO Gallery](http://isspns-container.issp.u-tokyo.ac.jp/repo/12).
## License
PHYSBO was developed based on [COMBO](https://github.com/tsudalab/COMBO) for academic use.
PHYSBO v2 is distributed under Mozilla Public License version 2.0 (MPL v2).
We hope that you cite the following reference when you publish the results using PHYSBO:
[“Bayesian optimization package: PHYSBO”, Yuichi Motoyama, Ryo Tamura, Kazuyoshi Yoshimi, Kei Terayama, Tsuyoshi Ueno, Koji Tsuda, Computer Physics Communications Volume 278, September 2022, 108405.](https://doi.org/10.1016/j.cpc.2022.108405)
Bibtex
```
@misc{@article{MOTOYAMA2022108405,
title = {Bayesian optimization package: PHYSBO},
journal = {Computer Physics Communications},
volume = {278},
pages = {108405},
year = {2022},
issn = {0010-4655},
doi = {https://doi.org/10.1016/j.cpc.2022.108405},
author = {Yuichi Motoyama and Ryo Tamura and Kazuyoshi Yoshimi and Kei Terayama and Tsuyoshi Ueno and Koji Tsuda},
keywords = {Bayesian optimization, Multi-objective optimization, Materials screening, Effective model estimation}
}
```
### Copyright
© *2020- The University of Tokyo. All rights reserved.*
This software was developed with the support of \"*Project for advancement of software usability in materials science*\" of The Institute for Solid State Physics, The University of Tokyo.