def test_abo_params_setter():
new_abo = abo.ABO()
try:
new_abo.sunspot_ratio = 'a'
except:
new_abo.sunspot_ratio = 0.9
try:
new_abo.sunspot_ratio = -1
except:
new_abo.sunspot_ratio = 0.9
assert new_abo.sunspot_ratio == 0.9
try:
new_abo.a = 'b'
except:
new_abo.a = 2.0
try:
new_abo.a = -1
except:
new_abo.a = 2.0
assert new_abo.a == 2.0
def test_abo_params():
params = {'sunspot_ratio': 0.9, 'a': 2.0}
new_abo = abo.ABO(params=params)
assert new_abo.sunspot_ratio == 0.9
assert new_abo.a == 2.0
def test_abo_params():
params = {"sunspot_ratio": 0.9, "a": 2.0}
new_abo = abo.ABO(params=params)
assert new_abo.sunspot_ratio == 0.9
assert new_abo.a == 2.0
def test_abo_flight_mode():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(
n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]
)
new_abo = abo.ABO()
new_abo._flight_mode(search_space.agents[0], search_space.agents[1], square)
def test_abo_hyperparams():
hyperparams = {
'sunspot_ratio': 0.9,
'a': 2.0
}
new_abo = abo.ABO(hyperparams=hyperparams)
assert new_abo.sunspot_ratio == 0.9
assert new_abo.a == 2.0
def test_abo_update():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(
n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]
)
new_abo = abo.ABO()
new_abo.update(search_space, square, 1, 10)
new_abo.update(search_space, square, 5, 10)
def test_abo_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_abo = abo.ABO()
search_space = search.SearchSpace(n_agents=10, n_iterations=100,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_abo.run(search_space, new_function, pre_evaluation=hook)
assert len(history.agents) > 0
assert len(history.best_agent) > 0
best_fitness = history.best_agent[-1][1]
assert best_fitness <= constants.TEST_EPSILON, 'The algorithm abo failed to converge.'
def test_abo_build():
new_abo = abo.ABO()
assert new_abo.built == True
