Metadata-Version: 2.1
Name: coco-experiment
Version: 2.8.0
Summary: Benchmarking framework for all types of black-box optimization algorithms.
Author: Asma Atamna, Dimo Brockhoff, Lorenzo Consoli, Ouassim Ait ElHara, Paul Dufossé, Nikolaus Hansen, Leopold Luley, Olaf Mersmann, Duc Manh Nguyen, Phillipe R. Sampaio, Marc Schoenauer, Dejan Tušar, Tea Tušar, Konstantinos Varelas
License: BSD-3-Clause
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: Implementation :: CPython
Project-URL: Homepage, https://github.com/numbbo/coco-experiment
Project-URL: Documentation, https://numbbo.github.io/coco-doc/apidocs/cocoex/
Project-URL: Issues, https://github.com/numbbo/coco-experiment/issues
Requires-Python: >=3.8
Requires-Dist: numpy>=1.24.0
Description-Content-Type: text/markdown

<h1 align="center">
    <table border="0">
  <td>
      <img src="https://raw.githubusercontent.com/numbbo/coco-experiment/main/logo/coco-ex-300.webp">          
  </td>
  <td>
      COmparing Continuous Optimisers COCO Experiment
</td>
</table>
</h1>

[![version](https://img.shields.io/pypi/v/coco-experiment?color=blue)](https://pypi.org/project/coco-experiment)
[![license](https://img.shields.io/pypi/l/coco-experiment)](https://pypi.org/project/coco-experiment)
[![build](https://img.shields.io/github/actions/workflow/status/numbbo/coco-experiment/build.yml?branch=main)][build]
[![download](https://img.shields.io/pypi/dm/coco-experiment.svg?label=PyPI%20downloads)][pypi]
[![paper](https://img.shields.io/badge/DOI-10.1080%2F10556788.2020.1808977-blue)][paper]

The `coco-experiment` Python module implements the experimental part of [COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting][paper].
It provides several benchmark function suites and the data logging.

## Documentation

The main documentation pages for the `coco-experiment` package `cocoex` can be found at

- [getting-started](https://numbbo.it/getting-started#experiment)
- [API documentation](https://numbbo.github.io/coco-doc/apidocs/cocoex)
- [issue tracker and bug reports](https://github.com/numbbo/coco-experiment/issues)
- [source code page](https://github.com/numbbo/coco-experiment) on GitHub
- [COCO home page](https://coco-platform.org)

Below are installation instruction and some usage hints.

## Installation

The easiest way to install coco-experiment is using `pip` (preferably in a virtual environment):

```python
pip install -U coco-experiment
```

For the difficult way [look at this page][codepython] and start with `git clone https://github.com/numbbo/coco-experiment.git`.

## Usage

Similar to `scikit-learn`, _the module name_ (`coco-experiment`) **is not the import name**, hence usage starts with

```python
import cocoex
```

To get started, take a look at the [getting-started] page and at this [simple example experiment][expy]. To run an experiment in practice, take a look at the [complete example experiment][expy2].

[build]: https://github.com/numbbo/coco-experiment/actions?query=branch%3Amain
[pypi]: https://pypi.org/project/coco-experiment/
[paper]: https://doi.org/10.1080/10556788.2020.1808977
[getting-started]: https://coco-platform.org/getting-started/#experiment
[expy]: https://github.com/numbbo/coco-experiment/blob/main/build/python/example/example_experiment_simple.py
[expy2]: https://github.com/numbbo/coco-experiment/blob/main/build/python/example/example_experiment_complete.py
[codepython]: https://github.com/numbbo/coco-experiment/tree/main/build
