#8787th of 168 in ML frameworks and libraries
bayesianoptimization
Gaussian-process Bayesian optimization of black-box functions in pure Python
- Stars
- 8.7k
- License
- MIT
- Last commit
- Oct 2026
- Last release
- Oct 2026
- Language
- Python
8,560 on 21 Apr 20268,734 on 10 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
A Python library that maximizes an unknown function within bounded parameters, using a Gaussian process and an acquisition strategy such as UCB or EI to choose each next point. You pass a function and a pbounds dict, then call maximize with init_points and n_iter. It suits expensive evaluations such as hyperparameter tuning, and the README cites support for constrained optimization and non-float parameters.
Who it is for: Python users tuning expensive black-box functions or hyperparameters
Strengths
- Minimal API: a function plus parameter bounds, then maximize()
- Pure Python, installs from PyPI or conda-forge
- Supports inequality constraints and domain reduction via SequentialDomainTransformer
- Works on any function returning a real number, not tied to ML
Weaknesses
- Bounds are required for every parameter
- Only maximizes; minimizing means negating the target
- Non-float parameter support is described only through a citation in the README
- README example is a toy function; no benchmarks or scaling limits given
What it needs
- no GPU
Quick answers
Is bayesianoptimization open source?
Yes. Its code is on GitHub under the MIT license.
Does bayesianoptimization need a GPU?
No, it runs without one.
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