def test_attributes_result_errors_1():
with pytest.raises(ValueError):
hyper = Hyperactive()
hyper.add_search(objective_function, search_space, n_iter=15)
hyper.run()
hyper.best_score(objective_function1)
def test_random_state_2():
hyper0 = Hyperactive()
hyper0.add_search(
objective_function,
search_space,
n_iter=10,
initialize={"random": 1},
random_state=1,
)
hyper0.run()
hyper1 = Hyperactive()
hyper1.add_search(
objective_function,
search_space,
n_iter=10,
initialize={"random": 1},
random_state=10,
)
hyper1.run()
best_score0 = hyper0.best_score(objective_function)
best_score1 = hyper1.best_score(objective_function)
assert abs(best_score0 - best_score1) > err
def test_max_score_0():
def objective_function(para):
score = -para["x1"] * para["x1"]
return score
search_space = {
"x1": np.arange(0, 100, 0.1),
}
max_score = -9999
opt = HillClimbingOptimizer(
epsilon=0.01,
rand_rest_p=0,
)
hyper = Hyperactive()
hyper.add_search(
objective_function,
search_space,
optimizer=opt,
n_iter=100000,
initialize={"warm_start": [{
"x1": 99
}]},
max_score=max_score,
)
hyper.run()
print("\n Results head \n", hyper.results(objective_function).head())
print("\n Results tail \n", hyper.results(objective_function).tail())
print("\nN iter:", len(hyper.results(objective_function)))
assert -100 > hyper.best_score(objective_function) > max_score
def test_attributes_best_score_objective_function_0():
hyper = Hyperactive()
hyper.add_search(
objective_function,
search_space,
n_iter=15,
)
hyper.run()
assert isinstance(hyper.best_score(objective_function), numbers.Number)
def test_initialize_vertices():
initialize = {"vertices": 2}
hyper = Hyperactive()
hyper.add_search(
objective_function, search_space, n_iter=2, initialize=initialize,
)
hyper.run()
assert abs(hyper.best_score(objective_function)) - 10000 < 0.001
def test_initialize_grid_0():
search_space = {
"x1": np.arange(-1, 2, 1),
}
initialize = {"grid": 1}
hyper = Hyperactive()
hyper.add_search(
objective_function, search_space, n_iter=1, initialize=initialize,
)
hyper.run()
assert abs(hyper.best_score(objective_function)) < 0.001
def test_initialize_warm_start_0():
init = {
"x1": 0,
}
initialize = {"warm_start": [init]}
hyper = Hyperactive()
hyper.add_search(
objective_function, search_space, n_iter=1, initialize=initialize,
)
hyper.run()
assert abs(hyper.best_score(objective_function)) < 0.001
def test_best_results_0(Optimizer, search_space, objective):
search_space = search_space
objective_function = objective
initialize = {"vertices": 2}
hyper = Hyperactive()
hyper.add_search(
objective_function,
search_space,
optimizer=Optimizer(),
n_iter=10,
memory=False,
initialize=initialize,
)
hyper.run()
assert hyper.best_score(objective_function) == objective_function(
hyper.best_para(objective_function))
