def test_sin_one(self):
f = synthetic_functions.SinOne()
for x in f.get_meta_information()["optima"]:
np.testing.assert_approx_equal(f(x),
f.get_meta_information()["f_opt"],
significant=9)
class SinOne(AbstractFunction):
_sin_one = hpobench.SinOne()
ORIGINAL_MIN_ARGUMENT = np.array(_sin_one.get_meta_information()["optima"])
ORIGINAL_MAX_ARGUMENT = np.array([[0.86743372]])
ORIGINAL_UPPER_BOUNDS = np.array([1.])
ORIGINAL_LOWER_BOUNDS = np.array([0.])
ORIGINAL_MAX = _sin_one.objective_function(
ORIGINAL_MAX_ARGUMENT[0])["function_value"]
ORIGINAL_MIN = _sin_one.get_meta_information()["f_opt"]
INVERT = True
@classmethod
def base_function(cls, x):
return cls._sin_one.objective_function(x)["function_value"]
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),
))
CONFIGURATIONS = tuple(({
"benchmark": benchmark,
"method": method
output_path = "./experiments/RoBO/synthetic_funcs/"
if benchmark == "branin":
b = hpobench.Branin()
elif benchmark == "hartmann3":
b = hpobench.Hartmann3()
elif benchmark == "hartmann6":
b = hpobench.Hartmann6()
elif benchmark == "camelback":
b = hpobench.Camelback()
elif benchmark == "goldstein_price":
b = hpobench.GoldsteinPrice()
elif benchmark == "rosenbrock":
b = hpobench.Rosenbrock()
elif benchmark == "sin_one":
b = hpobench.SinOne()
elif benchmark == "sin_two":
b = hpobench.SinTwo()
elif benchmark == "bohachevsky":
b = hpobench.Bohachevsky()
elif benchmark == "levy":
b = hpobench.Levy()
info = b.get_meta_information()
scenario = Scenario({
"run_obj": "quality",
"runcount-limit": n_iters,
"cs": b.get_configuration_space(),
"deterministic": "true",
"initial_incumbent": "RANDOM",
def __init__(self, path=None):
super().__init__(synthetic_functions.SinOne(), path)