def test_attributes_result_errors_0():
with pytest.raises(ValueError):
hyper = Hyperactive()
hyper.add_search(objective_function, search_space, n_iter=15)
hyper.run()
hyper.best_para(objective_function1)
def test_search_space_3():
def func1():
pass
def func2():
pass
def func3():
pass
def objective_function(opt):
score = -opt["x1"] * opt["x1"]
return score
search_space = {
"x1": list(range(0, 100, 1)),
"func1": [func1, func2, func3],
}
hyper = Hyperactive()
hyper.add_search(
objective_function,
search_space,
n_iter=15,
)
hyper.run()
assert isinstance(hyper.results(objective_function), pd.DataFrame)
assert (hyper.best_para(objective_function)["func1"]
in search_space["func1"])
def test_search_space_4():
class class1:
pass
class class2:
pass
class class3:
pass
def objective_function(opt):
score = -opt["x1"] * opt["x1"]
return score
search_space = {
"x1": list(range(0, 100, 1)),
"class1": [class1, class2, class3],
}
hyper = Hyperactive()
hyper.add_search(
objective_function,
search_space,
n_iter=15,
)
hyper.run()
assert isinstance(hyper.results(objective_function), pd.DataFrame)
assert (hyper.best_para(objective_function)["class1"]
in search_space["class1"])
def test_attributes_best_para_objective_function_0():
hyper = Hyperactive()
hyper.add_search(
objective_function,
search_space,
n_iter=15,
)
hyper.run()
assert isinstance(hyper.best_para(objective_function), dict)
def test_attributes_best_para_search_id_1():
hyper = Hyperactive()
hyper.add_search(
objective_function,
search_space,
search_id="1",
n_iter=15,
)
hyper.add_search(
objective_function1,
search_space,
search_id="2",
n_iter=15,
)
hyper.run()
assert isinstance(hyper.best_para("1"), dict)
def test_initialize_warm_start_1():
search_space = {
"x1": np.arange(-10, 10, 1),
}
init = {
"x1": -10,
}
initialize = {"warm_start": [init]}
hyper = Hyperactive()
hyper.add_search(
objective_function, search_space, n_iter=1, initialize=initialize,
)
hyper.run()
assert hyper.best_para(objective_function) == init
def test_search_space_0():
def objective_function(opt):
score = -opt["x1"] * opt["x1"]
return score
search_space = {
"x1": list(range(0, 3, 1)),
}
hyper = Hyperactive()
hyper.add_search(
objective_function, search_space, n_iter=15,
)
hyper.run()
assert isinstance(hyper.results(objective_function), pd.DataFrame)
assert hyper.best_para(objective_function)["x1"] in search_space["x1"]
def test_best_results_1(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_para(objective_function)["x1"] in list(
hyper.results(objective_function)["x1"])
def test_best_results_0(Optimizer, objective):
search_space = objective[1]
objective_function = objective[0]
initialize = {"vertices": 2}
hyper = Hyperactive()
hyper.add_search(
objective_function,
search_space,
optimizer=Optimizer(),
n_iter=30,
memory=False,
initialize=initialize,
)
hyper.run()
assert hyper.best_score(objective_function) == objective_function(
hyper.best_para(objective_function)
)
def test_best_results_1(Optimizer):
search_space = {"x1": np.arange(-100, 101, 1)}
def objective_function(opt):
score = -opt["x1"] * opt["x1"]
return score
initialize = {"vertices": 2}
hyper = Hyperactive()
hyper.add_search(
objective_function,
search_space,
optimizer=Optimizer(),
n_iter=30,
memory=False,
initialize=initialize,
)
hyper.run()
assert hyper.best_para(objective_function)["x1"] in list(
hyper.results(objective_function)["x1"]
)
