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):

Installation

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

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:

>>> from yabox.problems import Levy
>>> problem = Levy()
>>> problem.plot3d()

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

>>> 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:

>>> 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)

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.