https://github.com/automl/RoBO

RoBO: a Robust Bayesian Optimization framework
https://github.com/automl/RoBO

Last synced: 3 months ago
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RoBO: a Robust Bayesian Optimization framework

• Host: GitHub
• URL: https://github.com/automl/RoBO
• Owner: automl
• License: bsd-3-clause
• Created: 2015-03-27T09:02:20.000Z (almost 10 years ago)
• Default Branch: master
• Last Pushed: 2019-04-30T08:47:29.000Z (over 5 years ago)
• Last Synced: 2024-08-01T23:31:14.466Z (6 months ago)
• Language: Python
• Homepage:
• Size: 22.3 MB
• Stars: 480
• Watchers: 40
• Forks: 133
• Open Issues: 20
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Awesome Lists containing this project

• awesome-llmops - RoBO - square) | (AutoML / Profiling)

README

        
RoBO - a Robust Bayesian Optimization framework.

================================================

Master Branch

------------------

[![Build Status](https://travis-ci.org/automl/RoBO.svg?branch=master)](https://travis-ci.org/automl/RoBO)

[![Coverage Status](https://coveralls.io/repos/github/automl/RoBO/badge.svg?branch=master)](https://coveralls.io/github/automl/RoBO?branch=master)

[![Code Health](https://landscape.io/github/automl/RoBO/master/landscape.svg?style=flat)](https://landscape.io/github/automl/RoBO/master)

Installation

------------

RoBO uses the Gaussian processes library [george](https://github.com/automl/george.git) and the random forests library [pyrfr](https://github.com/automl/random_forest_run). In order to use these libraries make sure that libeigen and swig are installed:

```

sudo apt-get install libeigen3-dev swig 

```

Download RoBO and then change into the new directory:

```

git clone https://github.com/automl/RoBO

cd RoBO/

```

Install the required dependencies.

```

for req in $(cat requirements.txt); do pip install $req; done

```

Finally install RoBO by:

```

python setup.py install

```

Documentation

-------------

You can find the documentation for RoBO here http://automl.github.io/RoBO/

Citing RoBO

-----------

To cite RoBO please reference our BayesOpt paper:

```

@INPROCEEDINGS{klein-bayesopt17,

author    = {A. Klein and S. Falkner and N. Mansur and F. Hutter},

title     = {RoBO: A Flexible and Robust Bayesian Optimization Framework in Python},

booktitle = {NIPS 2017 Bayesian Optimization Workshop},

year      = {2017},

month     = dec,

}

```