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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import numpy as np
from gradient_free_optimizers import BayesianOptimizer
from ._base_test import _base_test
n_iter = 33
opt = BayesianOptimizer
def get_score(para):
return -(para["x1"] * para["x1"])
def test_skip_retrain():
for skip_retrain in ["many", "some", "few", "never"]:
opt_para = {"skip_retrain": skip_retrain}
_base_test(opt, n_iter, opt_para=opt_para)
def test_warm_start_smbo():
gpr_X, gpr_y = [], []
for _ in range(10):
pos_ = np.random.randint(0, high=9)
pos = np.array([pos_])
para = {
"x1": pos_,
}
gpr_X.append(pos)
gpr_y.append(get_score(para))
for warm_start_smbo in [None, (gpr_X, gpr_y)]:
opt_para = {"warm_start_smbo": warm_start_smbo}
def test_max_sample_size():
for max_sample_size in [10, 100, 10000, 10000000000]:
opt_para = {"max_sample_size": max_sample_size}
SimonBlanke /
Gradient-Free-Optimizers
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