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import numpy as np
import pytest
from ._parametrize import optimizers
def objective_function(para):
score = -para["x1"] * para["x1"]
return score
search_space = {
"x1": np.arange(-10, 10, 0.1),
}
@pytest.mark.parametrize(*optimizers)
def test_search_step_0(Optimizer):
n_iter = 100
opt = Optimizer(search_space)
opt.init_search(
objective_function,
n_iter,
max_time=None,
max_score=None,
early_stopping=None,
memory=True,
memory_warm_start=None,
verbosity=["progress_bar", "print_results", "print_times"],
)
for nth_iter in range(n_iter):
opt.search_step(nth_iter)
opt.finish_search()
SimonBlanke /
Gradient-Free-Optimizers
