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BayesO

BayesO: A Bayesian optimization framework in Python - Published in JOSS (2023)

https://github.com/jungtaekkim/bayeso

Science Score: 98.0%

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

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 4 DOI reference(s) in README and JOSS metadata
  • ✓
    Academic publication links
    Links to: joss.theoj.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ✓
    JOSS paper metadata
    Published in Journal of Open Source Software

Keywords

bayesian-optimization hyperparameter-optimization machine-learning

Scientific Fields

Artificial Intelligence and Machine Learning Computer Science - 31% confidence
Last synced: 6 months ago · JSON representation ·

Repository

Simple, but essential Bayesian optimization package

Basic Info
  • Host: GitHub
  • Owner: jungtaekkim
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage: https://bayeso.org
  • Size: 5.36 MB
Statistics
  • Stars: 94
  • Watchers: 5
  • Forks: 9
  • Open Issues: 1
  • Releases: 9
Topics
bayesian-optimization hyperparameter-optimization machine-learning
Created about 8 years ago · Last pushed 7 months ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

BayesO: A Bayesian Optimization Framework in Python

DOI Build Status Coverage Status PyPI - Python Version License: MIT Documentation Status

Simple, but essential Bayesian optimization package.

Installation

We recommend installing it with virtualenv. You can choose one of three installation options.

  • Using PyPI repository (for user installation)

To install the released version in PyPI repository, command it.

shell $ pip install bayeso

  • Using source code (for developer installation)

To install bayeso from source code, command the following in the bayeso root.

shell pip install .

  • Using source code (for editable development mode)

To use editable development mode, command the following in the bayeso root.

shell pip install -e .

If you want to install the packages required for optional features, development, and examples, you can simply add [optional], [dev], and [examples]. For example, pip install .[dev] or pip install -e .[dev].

  • Uninstallation

If you would like to uninstall bayeso, command it.

shell $ pip uninstall bayeso

Supported Python Version

We test our package in the following versions.

  • Python 3.7
  • Python 3.8
  • Python 3.9
  • Python 3.10
  • Python 3.11

Examples and Tests

We provide a list of examples and a list of tests.

Citation

@article{KimJ2023joss, author={Kim, Jungtaek and Choi, Seungjin}, title={{BayesO}: A {Bayesian} optimization framework in {Python}}, journal={Journal of Open Source Software}, volume={8}, number={90}, pages={5320}, year={2023} }

License

MIT License

Owner

  • Name: Jungtaek Kim
  • Login: jungtaekkim
  • Kind: user
  • Location: United States

JOSS Publication

BayesO: A Bayesian optimization framework in Python
Published
October 09, 2023
DOI
Volume 8, Issue 90, Page 5320
Authors
Jungtaek Kim ORCID
University of Pittsburgh, USA
Seungjin Choi ORCID
Intellicode, South Korea
Editor
Vincent Knight ORCID
Tags
Bayesian optimization global optimization black-box optimization optimization
View PDF Review Thread Software Archive

Citation (CITATION.cff) 

cff-version: "1.2.0"
authors:
- family-names: Kim
  given-names: Jungtaek
  orcid: "https://orcid.org/0000-0002-1905-1399"
- family-names: Choi
  given-names: Seungjin
  orcid: "https://orcid.org/0000-0002-7873-4616"
doi: 10.5281/zenodo.8419023
message: If you use this software, please cite our article in the
  Journal of Open Source Software.
preferred-citation:
  authors:
  - family-names: Kim
    given-names: Jungtaek
    orcid: "https://orcid.org/0000-0002-1905-1399"
  - family-names: Choi
    given-names: Seungjin
    orcid: "https://orcid.org/0000-0002-7873-4616"
  date-published: 2023-10-09
  doi: 10.21105/joss.05320
  issn: 2475-9066
  issue: 90
  journal: Journal of Open Source Software
  publisher:
    name: Open Journals
  start: 5320
  title: "BayesO: A Bayesian optimization framework in Python"
  type: article
  url: "https://joss.theoj.org/papers/10.21105/joss.05320"
  volume: 8
title: "BayesO: A Bayesian optimization framework in Python"

GitHub Events

Total
  • Watch event: 2
  • Push event: 53
Last Year
  • Watch event: 2
  • Push event: 53

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 562
  • Total Committers: 2
  • Avg Commits per committer: 281.0
  • Development Distribution Score (DDS): 0.002
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Jungtaek Kim j****m@p****r 561
Jungtaek Kim j****m@j****t 1
Committer Domains (Top 20 + Academic)
jt-inc.net: 1 postech.ac.kr: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 7
  • Total pull requests: 29
  • Average time to close issues: 26 days
  • Average time to close pull requests: 15 minutes
  • Total issue authors: 3
  • Total pull request authors: 1
  • Average comments per issue: 1.14
  • Average comments per pull request: 1.03
  • Merged pull requests: 29
  • 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
  • thomaspinder (5)
  • KOLANICH (1)
  • MarcosCarreira (1)
Pull Request Authors
  • jungtaekkim (30)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 2
  • Total downloads:
    • pypi 266 last-month
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 1
    (may contain duplicates)
  • Total versions: 25
  • Total maintainers: 1
proxy.golang.org: github.com/jungtaekkim/bayeso
  • Versions: 9
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 6.5%
Average: 6.7%
Dependent repos count: 6.9%
Last synced: 6 months ago
pypi.org: bayeso

Simple, but essential Bayesian optimization package

  • Versions: 16
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 266 Last month
  • Docker Downloads: 0
Rankings
Docker downloads count: 3.5%
Dependent packages count: 10.0%
Average: 13.8%
Downloads: 20.1%
Dependent repos count: 21.7%
Maintainers (1)
jungtaekkim
Last synced: 6 months ago

Dependencies

requirements-dev.txt pypi
  • coveralls * development
  • jupyter * development
  • pylint * development
  • pytest * development
  • pytest-benchmark * development
  • pytest-timeout * development
  • sphinx * development
  • sphinx_rtd_theme * development
requirements-examples.txt pypi
  • scikit-learn *
  • xgboost *
requirements-optional.txt pypi
  • bayeso-benchmarks *
  • matplotlib *
  • scipydirect *
requirements.txt pypi
  • cma *
  • numpy *
  • qmcpy *
  • scipy *
  • tqdm *
.github/workflows/codeql-analysis.yml actions
  • actions/checkout v2 composite
  • github/codeql-action/analyze v1 composite
  • github/codeql-action/autobuild v1 composite
  • github/codeql-action/init v1 composite
.github/workflows/pylint.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.github/workflows/pytest.yml actions
  • AndreMiras/coveralls-python-action v20201129 composite
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
setup.py pypi