{"id":13590217,"url":"https://github.com/issp-center-dev/PHYSBO","last_synced_at":"2025-04-08T12:33:04.120Z","repository":{"id":37100888,"uuid":"313533811","full_name":"issp-center-dev/PHYSBO","owner":"issp-center-dev","description":"PHYSBO -- optimization tools for PHYsics based on Bayesian Optimization","archived":false,"fork":false,"pushed_at":"2025-01-30T01:41:40.000Z","size":17486,"stargazers_count":74,"open_issues_count":5,"forks_count":19,"subscribers_count":6,"default_branch":"master","last_synced_at":"2025-04-02T01:19:05.182Z","etag":null,"topics":["bayesian-optimization"],"latest_commit_sha":null,"homepage":"https://www.pasums.issp.u-tokyo.ac.jp/physbo/en","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/issp-center-dev.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-11-17T06:59:25.000Z","updated_at":"2025-03-29T09:17:27.000Z","dependencies_parsed_at":"2024-06-04T02:38:36.294Z","dependency_job_id":"77649463-f270-4489-b044-4446eb5efd94","html_url":"https://github.com/issp-center-dev/PHYSBO","commit_stats":null,"previous_names":[],"tags_count":9,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/issp-center-dev%2FPHYSBO","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/issp-center-dev%2FPHYSBO/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/issp-center-dev%2FPHYSBO/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/issp-center-dev%2FPHYSBO/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/issp-center-dev","download_url":"https://codeload.github.com/issp-center-dev/PHYSBO/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247843113,"owners_count":21005407,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bayesian-optimization"],"created_at":"2024-08-01T16:00:41.683Z","updated_at":"2025-04-08T12:32:59.089Z","avatar_url":"https://github.com/issp-center-dev.png","language":"Python","funding_links":[],"categories":["Software"],"sub_categories":["Optimization"],"readme":"# optimization tools for PHYsics based on Bayesian Optimization ( PHYSBO )\n\nBayesian optimization has been proven as an effective tool in accelerating scientific discovery.\nA standard implementation (e.g., scikit-learn), however, can accommodate only small training data.\nPHYSBO 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).\nPHYSBO was developed based on [COMBO](https://github.com/tsudalab/combo) for academic use.\n\n## Document\n\n- Stable (master branch)\n    - [English](https://issp-center-dev.github.io/PHYSBO/manual/master/en/index.html)\n    - [日本語](https://issp-center-dev.github.io/PHYSBO/manual/master/ja/index.html)\n- Latest (develop branch)\n    - [English](https://issp-center-dev.github.io/PHYSBO/manual/develop/en/index.html)\n    - [日本語](https://issp-center-dev.github.io/PHYSBO/manual/develop/ja/index.html)\n\n## Required Packages\n\n- Python \u003e= 3.6\n    - No longer tested with Python 3.6\n- NumPy \u003c 2.0.0\n- SciPy\n\n## Install\n\n- From PyPI (recommended)\n\n```bash\npython3 -m pip install physbo\n```\n\n- From source (for developers)\n    1. Update pip (\u003e= 19.0)\n\n        ```bash\n        python3 -m pip install -U pip\n        ```\n\n    1. Download or clone the github repository\n\n        ```\n        git clone https://github.com/issp-center-dev/PHYSBO\n        ```\n\n    1. Install via pip\n\n        ``` bash\n        # ./PHYSBO is the root directory of PHYSBO\n        # pip install options such as --user are avaiable\n\n        python3 -m pip install ./PHYSBO\n        ```\n\n    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.\n\n## Uninstall\n\n```bash\npython3 -m pip uninstall physbo\n```\n\n## Usage\n\n['examples/simple.py'](https://github.com/issp-center-dev/PHYSBO/examples/simple.py) is a simple example.\n\n## Data repository\n\nA 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).\n\n## License\n\nPHYSBO was developed based on [COMBO](https://github.com/tsudalab/COMBO) for academic use.\nPHYSBO v2 is distributed under Mozilla Public License version 2.0 (MPL v2).\nWe hope that you cite the following reference when you publish the results using PHYSBO:\n\n[“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)\n\nBibtex\n\n```\n@misc{@article{MOTOYAMA2022108405,\ntitle = {Bayesian optimization package: PHYSBO},\njournal = {Computer Physics Communications},\nvolume = {278},\npages = {108405},\nyear = {2022},\nissn = {0010-4655},\ndoi = {https://doi.org/10.1016/j.cpc.2022.108405},\nauthor = {Yuichi Motoyama and Ryo Tamura and Kazuyoshi Yoshimi and Kei Terayama and Tsuyoshi Ueno and Koji Tsuda},\nkeywords = {Bayesian optimization, Multi-objective optimization, Materials screening, Effective model estimation}\n}\n```\n\n### Copyright\n\n© *2020- The University of Tokyo. All rights reserved.*\nThis 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.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fissp-center-dev%2FPHYSBO","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fissp-center-dev%2FPHYSBO","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fissp-center-dev%2FPHYSBO/lists"}