optimization tool for PHYSics based on Bayesian Optimization
Project description
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 >= 3.6
- numpy
- scipy
Install
- From PyPI (recommended)
$ pip3 install physbo
- From source (for developers)
-
Update pip (>= 19.0)
$ pip3 install -U pip
-
Download or clone the github repository
$ git clone https://github.com/issp-center-dev/PHYSBO
-
Edit some files
-
Install via pip
# ./PHYSBO is the root directory of PHYSBO # pip install options such as --user are avaiable $ pip3 install ./PHYSBO
-
Note: Do not
import physbo
at the root directory of the repository becauseimport physbo
does not try to import the installed PHYSBO but one in the repository, which includes Cython codes not compiled.
-
Uninstall
$ pip3 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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for physbo-0.3.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 42d44aa164b112723440fb107ab3dfacc44b1a86b6c95ad0cfb3deb5a3266648 |
|
MD5 | 8d7577bd292872b27f186be59705e3df |
|
BLAKE2b-256 | ec66e8c60d68b51b334031522895d05f5fde2ed196c422e707a71e7f10328c09 |
Hashes for physbo-0.3.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | efd6db94becfc9b5871d929ae4979d0792402c054198424c20a4ce084a9db17a |
|
MD5 | 13743f0b3b7f668c400ae04a549bcd8c |
|
BLAKE2b-256 | 61cf1d3c1c8db28595ffd1d55082d8c204b27b78ed99ce671fe8b6aa24b2a6b2 |
Hashes for physbo-0.3.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | dc2a8a7589d06647a10d4064eabf23e2f004d8f7d8b45b288e3ed6b034a528b9 |
|
MD5 | 5103b755fb623b2208b83a5a71bfacaa |
|
BLAKE2b-256 | 46b54bc2c37ebad6cc1122c5a905007ff98dce7a78c3b11f5ef8ffbdf2ee9088 |
Hashes for physbo-0.3.0-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 30429d034903155de5e5a9133b7b3f65a63826ca935ff04de1ff3cfc48805f2d |
|
MD5 | 0bdfdb6e30d216abe9c98ace8123fb5b |
|
BLAKE2b-256 | cdef24d03dd96a8cad28b46e094c0126134c767fe17314d853a72b261beb134c |