Scaffolding for the Global Function Search optimizer from Dlib

artificial-intelligence, machine-learning, optimization, optimizer
pip install gfsopt==1.1.0



Documentation Status Latest Version

pip3 install --user gfsopt

Convenient scaffolding for the excellent Global Function Search (GFS) hyperparameter optimizer from the Dlib library.

Provides the following features:

  • Parallel optimization: Run multiple hyperparameter searches in parallel on multiple cores
  • Save and restore progress: Save/restore settings, parameters and optimization progress to/from file.
  • Average over multiple runs: Run a stochastic objective function using the same parameters multiple times and report the average to Dlib's Global Function Search. Useful in highly stochastic domains to avoid biasing the search towards lucky runs.

For theoretical background of GFS, see 'A Global Optimization Algorithm Worth Using' and Malherbe & Vayatis 2017: Global optimization of Lipschitz functions

Example usage

A basic example where we maximize obj_func with respect to y over 10 runs, with as many parallel processes as there are logical cores, and save progress to file.

from gfsopt import GFSOptimizer

def obj_func(x, y, pid):
    """"Function to be maximized (pid is iteration number)""""
    a = (1.5 - x + x * y)**2
    b = (2.25 - x + x * y * y)**2
    c = (2.625 - x + x * y * y * y)**2
    return -(a + b + c)
# For this example, we pretend that we want to keep 'x' fixed at 0.5
# while optimizing 'y' in the range -4.5 to 4.5
pp = {'x': 0.5}  # Fixed problem parameters
space = {'y': [-4.5, 4.5]}  # Parameters to optimize over
optimizer = GFSOptimizer(pp, space, fname="test.pkl")
# Will sample and test 'y' 10 times, then save results, progress and settings to file, n_sims=10)

For a more extensive example, see

Installation & Requirements

Requires Python >=3.6 and the following libraries:


To install, do:

pip3 install --user gfsopt


See for an example and for API documentation.