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import pytest
import numpy as np
from hyperactive import Hyperactive
from hyperactive.optimizers.strategies import CustomOptimizationStrategy
from hyperactive.optimizers import HillClimbingOptimizer
from ._parametrize import optimizers
def objective_function(opt):
score = -(opt["x1"] * opt["x1"] + opt["x2"] * opt["x2"])
return score
search_space = {
"x1": list(np.arange(-3, 3, 1)),
"x2": list(np.arange(-3, 3, 1)),
}
@pytest.mark.parametrize(*optimizers)
def test_strategy_combinations_0(Optimizer):
optimizer1 = Optimizer()
optimizer2 = HillClimbingOptimizer()
opt_strat = CustomOptimizationStrategy()
opt_strat.add_optimizer(optimizer1, duration=0.5)
opt_strat.add_optimizer(optimizer2, duration=0.5)
n_iter = 4
hyper = Hyperactive()
hyper.add_search(
objective_function,
search_space,
optimizer=opt_strat,
n_iter=n_iter,
memory=False,
initialize={"random": 1},
)
hyper.run()
search_data = hyper.search_data(objective_function)
optimizer1 = hyper.opt_pros[0].optimizer_setup_l[0]["optimizer"]
optimizer2 = hyper.opt_pros[0].optimizer_setup_l[1]["optimizer"]
assert len(search_data) == n_iter
assert len(optimizer1.search_data) == 2
assert len(optimizer2.search_data) == 2
assert optimizer1.best_score <= optimizer2.best_score
SimonBlanke /
Hyperactive
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