https://github.com/pablormier/yabox

Yet another black-box optimization library for Python
https://github.com/pablormier/yabox

algorithms black-box black-box-optimization data-science differential-evolution evolutionary-algorithms minimization optimization parallel python python3 stochastic-algorithms

Last synced: 1 day ago
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Yet another black-box optimization library for Python

• Host: GitHub
• URL: https://github.com/pablormier/yabox
• Owner: pablormier
• License: apache-2.0
• Created: 2017-07-14T12:57:43.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-12-06T09:07:39.000Z (about 2 years ago)
• Last Synced: 2024-12-24T00:12:29.778Z (8 days ago)
• Topics: algorithms, black-box, black-box-optimization, data-science, differential-evolution, evolutionary-algorithms, minimization, optimization, parallel, python, python3, stochastic-algorithms
• Language: Jupyter Notebook
• Homepage:
• Size: 1.58 MB
• Stars: 137
• Watchers: 5
• Forks: 15
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

        
# 

[![DOI](https://zenodo.org/badge/97233963.svg)](https://zenodo.org/badge/latestdoi/97233963)

_Yet another black-box optimization library for Python_

## Description

Yabox is a very small library for black-box (derivative free) optimization of functions that only depends on `numpy` and `matplotlib` for visualization. The library includes different stochastic algorithms for minimizing a function `f(X)`  that does not need to have an analytical form, where `X = {x1, ..., xN}`.

The current version of the library includes the Differential Evolution algorithm and a modified version for parallel evaluation.

Example of minimization of the Ackley function (using Yabox and Differential Evolution):

![Ackley Function](../master/notebooks/img/ackley.gif?raw=true)

## Installation

Yabox is in PyPI so you can use the following command to install the latest released version:

```bash

pip install yabox

```

## Basic usage

### Pre-defined functions

Yabox includes some default benchmark functions used in black-box optimization, available in the package yabox.problems. These functions also include 2D and 3D plotting capabilities:

```python

>>> from yabox.problems import Levy

>>> problem = Levy()

>>> problem.plot3d()

```

![Levy Function](../master/docs/images/levy.png?raw=true)

A problem is just a function that can be evaluated for a given X:

```python

>>> problem(np.array([1,1,1]))

0.80668910823394901

```

### Optimization

Simple example minimizing a function of one variable `x` using Differential Evolution, searching between -10 <= x <= 10:

```python

>>> from yabox import DE

>>> DE(lambda x: sum(x**2), [(-10, 10)]).solve()

(array([ 0.]), 0.0)

```

Example using Differential Evolution and showing progress (requires tqdm)

![Optimization example](../master/docs/images/opt_example.gif?raw=true)

Yabox includes a parallel version of Differential Evolution. Import PDE instead of DE:

```python

>>> from yabox import PDE

>>> PDE(lambda x: sum(x**2), [(-10, 10)]).solve()

(array([ 0.]), 0.0)

```

**For more examples, check the notebooks included in the project**

## About

This library is inspired in the scipy's differential evolution implementation. The main goal of Yabox is to include a larger set of stochastic black-box optimization algorithms plus many utilities, all in a small library with minimal dependencies.