gpyopt

Gaussian Process Optimization using GPy

https://github.com/sheffieldml/gpyopt

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
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codemeta.json file
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.zenodo.json file
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DOI references
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Academic publication links
✓
Committers with academic emails
6 of 50 committers (12.0%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (17.2%) to scientific vocabulary

Last synced: 11 months ago · JSON representation

Repository

Gaussian Process Optimization using GPy

Basic Info

Host: GitHub
Owner: SheffieldML
License: bsd-3-clause
Language: Jupyter Notebook
Default Branch: master
Size: 121 MB

Statistics

Stars: 947
Watchers: 43
Forks: 262
Open Issues: 104
Releases: 0

Archived

Created almost 12 years ago · Last pushed over 3 years ago

Metadata Files

Readme License

README.md

# End of maintenance for GPyOpt

Dear GPyOpt community!

We would like to acknowledge the obvious. The core team of GPyOpt has moved on, and over the past months we weren't giving the package nearly as much attention as it deserves. Instead of dragging our feet and giving people only occasional replies and no new features, we feel the time has come to officially declare the end of GPyOpt maintenance and archive this repository.

We would like to thank the community that has formed around GPyOpt. Without your interest, discussions, bug fixes and pull requests the package would never be as successful as it is. We hope we were able to provide you with a useful tool to aid your research and work.

If you feel really enthusiastic and would like to take over the package, feel free to drop us an email, and who knows, maybe you'll be the one(s) carrying the GPyOpt to new heights!

Sincerely yours, Andrei Paleyes and Javier Gonzalez

GPyOpt

Gaussian process optimization using GPy. Performs global optimization with different acquisition functions. Among other functionalities, it is possible to use GPyOpt to optimize physical experiments (sequentially or in batches) and tune the parameters of Machine Learning algorithms. It is able to handle large data sets via sparse Gaussian process models.

Citation

@Misc{gpyopt2016, author = {The GPyOpt authors}, title = {{GPyOpt}: A Bayesian Optimization framework in python}, howpublished = {\url{http://github.com/SheffieldML/GPyOpt}}, year = {2016} }

Getting started

Installing with pip

The simplest way to install GPyOpt is using pip. ubuntu users can do:

bash sudo apt-get install python-pip pip install gpyopt

If you'd like to install from source, or want to contribute to the project (e.g. by sending pull requests via github), read on. Clone the repository in GitHub and add it to your $PYTHONPATH.

bash git clone https://github.com/SheffieldML/GPyOpt.git cd GPyOpt python setup.py develop

Dependencies:

GPy
paramz
numpy
scipy
matplotlib
DIRECT (optional)
cma (optional)
pyDOE (optional)
sobol_seq (optional)

You can install dependencies by running: pip install -r requirements.txt

Funding Acknowledgements

BBSRC Project No BB/K011197/1 "Linking recombinant gene sequence to protein product manufacturability using CHO cell genomic resources"
See GPy funding Acknowledgements

Owner

Name: Sheffield Machine Learning Software
Login: SheffieldML
Kind: organization

Website: http://sheffieldml.github.io
Repositories: 54
Profile: https://github.com/SheffieldML

Software from the Sheffield machine learning group and collaborators.

GitHub Events

Total

Watch event: 20
Fork event: 4

Last Year

Watch event: 20
Fork event: 4

Committers

Last synced: almost 3 years ago

All Time

Total Commits: 470
Total Committers: 50
Avg Commits per committer: 9.4
Development Distribution Score (DDS): 0.402

Past Year

Commits: 1
Committers: 1
Avg Commits per committer: 1.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
javiergonzalezh	j**z@s**k	281
Zhenwen Dai	z**i@s**k	57
Zhenwen Dai	z**i@u**m	23
Andrei Paleyes	p**s@a**m	18
Andrei Paleyes	a**s@u**m	9
Javier Gonzalez	j**z@J**l	8
James Hensman	j**n@g**m	7
Aki Vehtari	A**i@a**i	4
Alan Saul	a**l@g**m	4
Gonzalez	g**v@a**m	4
Federico T	f**i@g**m	3
alaya	l**a@q**m	3
Julian Kuhlmann	j**n@s**m	3
Andreas	a**u@g**m	3
Felix Berkenkamp	f**p@g**m	3
Zhenwen Dai	z**d@s**m	2
Marcello	4**o@g**m	2
Andreas Mueller	t**t@g**m	2
sdr2002	s**2@g**m	2
Josh Fass	j**4@c**u	2
Miroslav Pištěk	m**k@g**m	1
Alan Saul	a**l@u**m	1
Barcelona	d**n@t**m	1
Mark Pullin	3**i@u**m	1
LEleonora	3**a@u**m	1
Keegan Harris	k**6@g**m	1
Matt Lavin	m**n@g**m	1
François Farquet	f**s@g**m	1
Javier González	j**1@g**m	1
Lander Bodyn	b**r@g**m	1
and 20 more...

Committer Domains (Top 20 + Academic)

amazon.com: 2 sheffield.ac.uk: 2 sourced.tech: 1 kamronn.dk: 1 fias.uni-frankfurt.de: 1 amazon.co.uk: 1 kit.edu: 1 ethz.ch: 1 typeform.com: 1 cornell.edu: 1 spotify.com: 1 stem.com: 1 qq.com: 1 aalto.fi: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time

Total issues: 81
Total pull requests: 19
Average time to close issues: 28 days
Average time to close pull requests: 6 months
Total issue authors: 62
Total pull request authors: 14
Average comments per issue: 3.52
Average comments per pull request: 2.68
Merged pull requests: 9
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

ekalosak (8)
lewisscola (7)
pavel-rev (4)
Abhiparth96 (2)
Paul1298 (2)
will-colea (2)
ktakihara2000 (1)
swapchavan (1)
Carldeboer (1)
vsariola (1)
cs071372 (1)
cmleecm (1)
samath117 (1)
devpouya (1)
WuSht (1)

Pull Request Authors

zhenwendai (4)
pavel-rev (2)
ekalosak (2)
Paul1298 (1)
julesjulian (1)
ianhojy (1)
javiergonzalezh (1)
LilyEvansHogwarts (1)
Xilorole (1)
jkaardal (1)
jhelsas (1)
komorihi (1)
KrzysztofNawara (1)
raff7 (1)

Top Labels

Issue Labels

Pull Request Labels

Packages

Total packages: 2
Total downloads:
- pypi 4,180 last-month

Total dependent packages: 5
(may contain duplicates)
Total dependent repositories: 38
(may contain duplicates)
Total versions: 12
Total maintainers: 3

pypi.org: gpyopt

The Bayesian Optimization Toolbox

Homepage: http://sheffieldml.github.io/GPyOpt/
Documentation: https://gpyopt.readthedocs.io/
License: bsd-3-clause
Latest release: 1.2.6
published over 6 years ago

Versions: 10
Dependent Packages: 4
Dependent Repositories: 35
Downloads: 4,180 Last month

Rankings

Dependent packages count: 1.8%

Stargazers count: 2.1%

Dependent repos count: 2.5%

Average: 2.6%

Downloads: 3.1%

Forks count: 3.2%

Maintainers (3)

apaleyes gojav zhenwen

Last synced: 11 months ago

conda-forge.org: gpyopt

Homepage: http://sheffieldml.github.io/GPyOpt/
License: BSD-3-Clause
Latest release: 1.2.6
published over 6 years ago

Versions: 2
Dependent Packages: 1
Dependent Repositories: 3

Rankings

Forks count: 11.2%

Stargazers count: 13.6%

Average: 17.9%

Dependent repos count: 17.9%

Dependent packages count: 29.0%

Last synced: 11 months ago

Dependencies

requirements.txt pypi

GPy >=1.8
PyDOE >=0.3.0
codecov *
cycler >=0.10.0
decorator >=4.0.10
emcee ==2.2.1
matplotlib >=1.5.3
mock >=2.0.0
nose *
numpy >=1.11.2
paramz >=0.7.0
python-dateutil >=2.6.0
scipy >=0.18.1
six >=1.10.0
sobol_seq >=0.1

setup.py pypi

GPy >=1.8
numpy >=1.7
scipy >=0.16