Physbo

Release notes

Release note
Changes from v0.3.x
New features
- `policy.get_post_fmean(xs)` (17)
- mean values of the trained predictor (the post-distribution of the Guassian process) at points `xs`
- `policy.get_post_fcov(xs)` (17)
- covariance of the trained predictor (the post-distribution of the Guassian process) at points `xs`
- `policy.get_score(mode, xs_or_actions)` (16)
- score (acquisition function) at points xs or actions

Changes
- Move `physbo.search.discrete.policy_mo` to `physbo.search.discrete_multi.policy` (13)

Fixes
- Return best actions as an array of integer (12)
- Fixed a bug of crashing if no actions remain (14)

Documents
- English manual is uploaded.

Release note
Changes from v0.2.x
New features
- Multi-objective optimization (Pareto optimization)
- Initialize `policy` (model) with pre-evaluated training datasets
- Parallelization for evaluating acquisition function (score) on each candidate (EXPERIMENTAL)
Documents
- Tutorials are updated
- Multi-objective optimization
- Initialize with pre-evaluated data
Others
- `pip install` from the local source code is enabled

Release Note
Change from v0.1.x
- 🎉 Support Python3 (`>= 3.6`)
- No longer support Python2 (Use v0.1.0)
- Manual, README, and sample files are updated.

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).
PHYSBO was developed based on [COMBO](https://github.com/tsudalab/combo) for academic use.

Document

- english (in preparation)
- [日本語](https://issp-center-dev.github.io/PHYSBO/manual/v0.1.0/ja/index.html)

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)
bash
$ pip2 install physbo


- From source (for developers)
1. Install NumPy and Cython before installing PHYSBO
bash
$ pip2 install numpy Cython


1. Download or clone the github repository

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


1. Run setup.py install
bash
$ cd physbo
$ python2 setup.py install --user


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
$ pip2 uninstall physbo


Usage
After installation, you can launch the test suite from ['examples/grain_bound/tutorial.ipynb'](examples/grain_bound/tutorial.ipynb).

License
PHYSBO was developed based on [COMBO](https://github.com/tsudalab/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.