def test_hartmann3(self):
f = synthetic_functions.Hartmann3()
for x in f.get_meta_information()["optima"]:
np.testing.assert_approx_equal(f(x),
f.get_meta_information()["f_opt"],
significant=9)
class Hartmann3D(AbstractFunction):
_hartmann = hpobench.Hartmann3()
ORIGINAL_MAX_ARGUMENT = np.array([[0., 0., 0.]])
ORIGINAL_MAX = _hartmann.objective_function(
ORIGINAL_MAX_ARGUMENT[0])["function_value"]
ORIGINAL_MIN = _hartmann.get_meta_information()["f_opt"]
ORIGINAL_MIN_ARGUMENT = np.array(
_hartmann.get_meta_information()["optima"])
ORIGINAL_UPPER_BOUNDS = np.array([1., 1., 1.])
ORIGINAL_LOWER_BOUNDS = np.array([0., 0., 0.])
INVERT = True
@classmethod
def base_function(cls, x):
return cls._hartmann.objective_function(x)["function_value"]
import numpy as np
import scipy.optimize as spopt
import hpolib.benchmarks.synthetic_functions as hpobench
# Run Scipy.minimize on artificial testfunctions
h3 = hpobench.Hartmann3()
h6 = hpobench.Hartmann6()
b = hpobench.Branin()
for f in [b, h3, h6]:
info = f.get_meta_information()
print("=" * 50)
print(info['name'])
bounds = np.array(info['bounds'])
res = spopt.minimize(f, (bounds[:, 0] + bounds[:, 1]) / 2,
bounds=bounds,
method='SLSQP')
assert (np.allclose(res.fun, info['f_opt']))
for o in info['optima']:
print("There is an optimum at \t "
"x={} with f(x) = {}".format(o, info['f_opt']))
print("scipy.optimize found \t x={} with f(x) = {}".format(res.x, res.fun))
sys.path.insert(0, path_join(dirname(__file__), "robo"))
sys.path.insert(0, path_join(dirname(__file__), "pysgmcmc_development"))
from collections import OrderedDict
from functools import partial
from itertools import product
from pysgmcmc_experiments.experiment_wrapper import to_experiment
import numpy as np
from robo.fmin import (bayesian_optimization, entropy_search, random_search,
bohamiann)
from robo.fmin.keras_bohamiann import bohamiann as keras_bohamiann
import hpolib.benchmarks.synthetic_functions as hpobench
BENCHMARKS = OrderedDict(
(("branin", hpobench.Branin()), ("hartmann3", hpobench.Hartmann3()),
("hartmann6", hpobench.Hartmann6()), ("camelback", hpobench.Camelback()),
("goldstein_price", hpobench.GoldsteinPrice()), ("rosenbrock",
hpobench.Rosenbrock()),
("sin_one", hpobench.SinOne()), ("sin_two", hpobench.SinTwo()),
("bohachevsky", hpobench.Bohachevsky()), ("levy", hpobench.Levy())))
METHODS = OrderedDict((
("rf", partial(bayesian_optimization, model_type="rf")),
("gp", partial(bayesian_optimization, model_type="gp")),
("gp_mcmc", partial(bayesian_optimization, model_type="gp_mcmc")),
("entropy_search", entropy_search),
("random_search", random_search),
("bohamiann", bohamiann),
("keras_bohamiann", keras_bohamiann),
))
def __init__(self, path=None):
super().__init__(synthetic_functions.Hartmann3(), path)