def test_rosenbrock(self):
f = synthetic_functions.Rosenbrock()
for x in f.get_meta_information()["optima"]:
np.testing.assert_approx_equal(f(x),
f.get_meta_information()["f_opt"],
significant=9)
class Rosenbrock2D(AbstractFunction):
_rosenbrock = hpobench.Rosenbrock()
ORIGINAL_MAX = 1102581
ORIGINAL_MIN = _rosenbrock.get_meta_information()["f_opt"]
ORIGINAL_MIN_ARGUMENT = np.array(
_rosenbrock.get_meta_information()["optima"])
ORIGINAL_MAX_ARGUMENT = np.array([[10., -5.]])
ORIGINAL_UPPER_BOUNDS = np.array([10., 10.])
ORIGINAL_LOWER_BOUNDS = np.array([-5., -5.])
INVERT = True
@classmethod
def base_function(cls, x):
return cls._rosenbrock.objective_function(x)["function_value"]
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),
))
CONFIGURATIONS = tuple(({
"benchmark": benchmark,
"ATPE": atpeOptimizer,
"TPE": tpeOptimizer,
"Random": randomOptimizer
}
# Run Scipy.minimize on artificial testfunctions
h3 = hpobench.Hartmann3()
h6 = hpobench.Hartmann6()
b = hpobench.Branin()
bo = hpobench.Bohachevsky()
cb = hpobench.Camelback()
fo = hpobench.Forrester()
gp = hpobench.GoldsteinPrice()
le = hpobench.Levy()
rb = hpobench.Rosenbrock()
logreg = svm_benchmark.SvmOnMnist()
for f in [logreg]:
info = f.get_meta_information()
print("=" * 50)
print(info['name'])
space = {"type": "object", "properties": {}}
for boundIndex, bound in enumerate(info['bounds']):
space['properties'][str(boundIndex)] = {
"type": "number",
"scaling": "linear",
def __init__(self, path=None):
super().__init__(synthetic_functions.Rosenbrock(), path)