Sambo
🎯 📈 Sequential and Model-Based Optimization in Python, featuring SCE-UA, SMBO, and SHGO algorithms. SOTA perfomance; 0 deps.
Install / Use
/learn @sambo-optimization/SamboAbout this skill
Quality Score
0/100
Category
Development & EngineeringSupported Platforms
Universal
Tags
bayesian-optimizationbayesoptblackbox-optimizationglobal-optimizationglobal-optimization-algorithmshydrological-modellinghydrologyhyperparameter-optimizationhyperparameter-searchhyperparameter-tuningoptimizationoptimization-algorithmsoptimization-toolsoptimizepartial-dependence-plotsce-uascientific-computingscikit-optimizescipy-optimizesurrogate-based-optimization
README
SAMBO: Sequential And Model-Based (Bayesian) Optimization of black-box objective functions.
Installation
$ pip install sambo
# or
$ pip install 'sambo[all]' # Pulls in Matplotlib, scikit-learn
Usage
See examples on the project website.
Features
- Python 3+
- Simple usage, standard API.
- Algorithms prioritize to minimize number of evaluations of the objective function: SHGO, SCE-UA and SMBO available.
- Minimal dependencies: NumPy, SciPy (scikit-learn & Matplotlib optional).
- State-of-the-art performance—see benchmark results against other common optimizer implementations.
- Integral, real (floating), and categorical dimensions.
- Fast approximate global black-box optimization.
- Beautiful Matplotlib charts.
Development
Check CONTRIBUTING.md for hacking details.
