https://github.com/tsudalab/combo

COMmon Bayesian Optimization
https://github.com/tsudalab/combo

Last synced: 3 months ago
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COMmon Bayesian Optimization

• Host: GitHub
• URL: https://github.com/tsudalab/combo
• Owner: tsudalab
• Created: 2016-01-26T02:30:20.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2021-01-24T23:46:27.000Z (almost 4 years ago)
• Last Synced: 2024-07-04T02:14:50.115Z (4 months ago)
• Language: Python
• Homepage:
• Size: 847 KB
• Stars: 144
• Watchers: 14
• Forks: 37
• Open Issues: 9
• Metadata Files:
  • Readme: README.md

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README

        
COMmon Bayesian Optimization Library ( COMBO )

========

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.

COMBO 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 [our document](/docs/combo_document.pdf).

# Required Packages ############################

* Python 2.7.x

* numpy  >=1.10

* scipy  >= 0.16

* Cython >= 0.22.1

* mpi4py >= 2.0 (optional)

# Install ######################################

	1. Download or clone the github repository, e.g.

		> git clone https://github.com/tsudalab/combo.git

	2. Run setup.py install

		> cd combo

		> python setup.py install

# Uninstall

	1. Delete all installed files, e.g.

		> python setup.py install --record file.txt

		> cat file.txt  | xargs rm -rvf

# Usage

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

## License

This package is distributed under the MIT License.