Tuning Benchmarks

This tutorial demonstrates how to use Tuun to optimize a few popular benchmark functions.

The following script shows how to run Tuun on the 6 dimensional Hartmann function. The full code for this example can be found here.

from tuun.main import Tuun
from hartmann import hartmann6

# configure Tuun
config = {
    'seed': 11,
    'acqfunction_config': {'name': 'default', 'acq_str': 'ei', 'n_gen': 500},
    'model_config': {'name': 'standistmatgp'},
}
tu = Tuun(config)

# set search space
search_space_list = [('real', [0, 1])] * 6
tu.set_config_from_list(search_space_list)

# define function to optimize
f = hartmann6

# minimize function over search space
result = tu.minimize_function(f, 60)

The following script shows how to run Tuun on a 40 dimensional version of the Branin function. The full code for this example can be found here.

import numpy as np
from tuun.main import Tuun
from examples.branin.branin import branin

config = {
    'seed': 11,
    'acqfunction_config': {'name': 'default', 'acq_str': 'ei', 'n_gen': 500},
    'acqoptimizer_config': {'name': 'neldermead', 'n_init_rs': 10},
    'model_config': {'name': 'standistmatgp'},
}
tu = Tuun(config)

search_space_list = [('real', [-5, 10]), ('real', [0, 15])] * 20
tu.set_config_from_list(search_space_list)

f = lambda x: np.sum([branin(x[2 * i : 2 * i + 2]) for i in range(20)])

result = tu.minimize_function(f, 120)

The plots below show a couple examples of Tuun, along with other tuning algorithms, on the above benchmark functions.