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import time
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
from gradient_free_optimizers import (
RandomSearchOptimizer,
HillClimbingOptimizer,
)
def objective_function(para):
score = -para["x1"] * para["x1"]
return score
search_space = {
"x1": np.arange(0, 100000, 0.1),
}
def test_max_time_0():
c_time1 = time.time()
opt = RandomSearchOptimizer(search_space)
opt.search(objective_function, n_iter=1000000, max_time=0.1)
diff_time1 = time.time() - c_time1
assert diff_time1 < 1
def test_max_time_1():
opt.search(objective_function, n_iter=1000000, max_time=1)
assert 0.3 < diff_time1 < 2
SimonBlanke /
Gradient-Free-Optimizers
