https://github.com/sambo-optimization/sambo

🎯📈 Sequantial and model-based optimization
https://github.com/sambo-optimization/sambo

bayesian-optimization bayesopt blackbox-optimization global-optimization global-optimization-algorithms hyperparameter-optimization hyperparameter-tuning machine-learning partial-dependence-plot sce-ua scientific-computing scikit-learn scikit-optimize scipy-optimize surrogate-based-optimization

Last synced: 4 days ago
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🎯📈 Sequantial and model-based optimization

• Host: GitHub
• URL: https://github.com/sambo-optimization/sambo
• Owner: sambo-optimization
• License: agpl-3.0
• Created: 2024-11-26T03:19:11.000Z (3 months ago)
• Default Branch: master
• Last Pushed: 2025-01-21T03:30:51.000Z (about 1 month ago)
• Last Synced: 2025-01-21T04:19:06.915Z (about 1 month ago)
• Topics: bayesian-optimization, bayesopt, blackbox-optimization, global-optimization, global-optimization-algorithms, hyperparameter-optimization, hyperparameter-tuning, machine-learning, partial-dependence-plot, sce-ua, scientific-computing, scikit-learn, scikit-optimize, scipy-optimize, surrogate-based-optimization
• Language: Python
• Homepage: https://sambo-optimization.github.io
• Size: 85.9 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.md

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README

        
[![SAMBO - Sequential And Model-Based Optimization](logo.svg)](https://sambo-optimization.github.io/)

=====

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SAMBO: Sequential And Model-Based (Bayesian) Optimization of black-box objective functions.

[**Project website**](https://sambo-optimization.github.io)

[Documentation]

[Documentation]: https://sambo-optimization.github.io/doc/sambo/

Installation

------------

```shell

$ pip install sambo

# or

$ pip install 'sambo[all]'   # Pulls in Matplotlib, scikit-learn

```

Usage

-----

See [examples on project website](https://sambo-optimization.github.io/#examples).

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](https://sambo-optimization.github.io/#benchmark)

  against other common optimizer implementations.

* Integral, real (floating), and categorical dimensions.

* Fast approximate global black-box optimization.

* [Beautiful Matplotlib charts](https://sambo-optimization.github.io/#examples).

Development

-----------

Check [CONTRIBUTING.md](CONTRIBUTING.md) for hacking details.