Yet another black-box optimization library for Python
Yet another black-box optimization library for Python
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):
Yabox is in PyPI so you can use the following command to install the latest released version:
pip install yabox
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
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)
Yabox includes a parallel version of Differential Evolution. Import PDE instead of DE:
>>> 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
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.