bayesian-optimization/bayesianoptimization

0

/ 100

Documentation82
Engineering94
Health98
Python8,652MIT7d ago
Grade a repo

A well-known project done right. Strong docs and solid engineering throughout.

A Python implementation of global optimization with gaussian processes.

Documentation

82

Contributing guide5pt0

No CONTRIBUTING.md found (−47 pts base + up to −53 pts more for content).

→ Add a CONTRIBUTING.md telling newcomers how to get involved. Include setup, code style, test, and PR instructions.

Install and run instructions9pt90

README documents how to install the project.

README12pt100

README is present.

License6pt100

Licensed under MIT.

Engineering

94

Reproducibility6pt70

Lockfile present (docsrc/requirements.txt). Installs are reproducible.

Tests18pt95

Test files detected (tests).

CI/CD14pt100

CI is configured (.github/workflows/build_docs.yml).

Linting and formatting5pt100

Linter or formatter configured (ruff.toml).

Issue and PR templates6pt100

Issue or PR templates present.

Project health

98

Dependency manifest6pt93

Dependency manifest found (pyproject.toml).

Repository metadata5pt100

Repository has a description.

Activity5pt100

Actively maintained (pushed within the last month).

Housekeeping3pt100

.gitignore present.

Repository health signals

Activity, community, and responsiveness at scan time

Activity

  • 5 / 7
    Commits (30d / 90d)
  • 1,602
    Forks
  • 28
    Releaseslatest 3y ago

Community

  • 50% - Fair
    Community health
  • 2 bus factor
    authors own >50% of commits
  • 8,652
    Watchers

Responsiveness

  • 5h
    Median issue response
  • 21h
    Median PR merge time
  • 8
    Open issues
Repository files12 root entries
  • .github
    Good: CI is configured (.github/workflows/build_docs.yml).
    Good: Issue or PR templates present.
  • bayes_opt
  • docsrc
    Good: Lockfile present (docsrc/requirements.txt). Installs are reproducible.
  • examples
  • scripts
  • tests
    Good: Test files detected (tests).
  • .gitignore
    Good: .gitignore present.
  • .pre-commit-config.yaml
  • LICENSE
    Good: Licensed under MIT.
  • pyproject.toml
    Good: Dependency manifest found (pyproject.toml).
  • README.md
    Good: README is present.
    Good: README is well structured with multiple sections.
    Good: README includes screenshots or visuals. Great for first impressions.
    Good: README has code examples.
    Good: README links to a live demo or deployed app.
    Good: README includes status badges.
    Good: README documents how to install the project.
    Good: README documents how to run the project.
  • ruff.toml
    Good: Linter or formatter configured (ruff.toml).