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.
PHYSBO was developed based on COMBO for academic use.

Document

• english (in preparation)
• 日本語

Required Packages

• Python 2.7.x
  • We plan to support Python 3.x in the next version of PHYSBO
• numpy
• scipy

Install

• From PyPI (recommended)

    $ pip2 install physbo
  

• From source (for developers)
  1. Install NumPy and Cython before installing PHYSBO

      $ pip2 install numpy Cython
     

  2. Download or clone the github repository

      $ git clone https://github.com/issp-center-dev/PHYSBO
     

  3. Run setup.py install

      $ cd physbo
      $ python2 setup.py install --user
     

  4. 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

$ pip2 uninstall physbo

Usage

After installation, you can launch the test suite from ‘examples/grain_bound/tutorial.ipynb’.

License

PHYSBO was developed based on COMBO for academic use.
This package is distributed under GNU General Public License version 3 (GPL v3) or later.

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.