def test_abc_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
hyperparams = {'n_trials': 1}
new_abc = abc.ABC(hyperparams=hyperparams)
search_space = search.SearchSpace(n_agents=10,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_abc.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 abc failed to converge.'
def test_abc_hyperparams():
hyperparams = {
'n_trials': 5
}
new_abc = abc.ABC(hyperparams=hyperparams)
assert new_abc.n_trials == 5
def test_abc_send_onlooker():
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_abc = abc.ABC()
new_abc.compile(search_space)
new_abc._send_onlooker(search_space.agents, square)
def test_abc_hyperparams_setter():
new_abc = abc.ABC()
try:
new_abc.n_trials = 0.0
except:
new_abc.n_trials = 10
try:
new_abc.n_trials = 0
except:
new_abc.n_trials = 10
assert new_abc.n_trials == 10
def test_abc_evaluate_location():
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_abc = abc.ABC()
new_abc.compile(search_space)
new_abc._evaluate_location(search_space.agents[0], search_space.agents[1],
square, 0)
def test_abc_compile():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_abc = abc.ABC()
new_abc.compile(search_space)
try:
new_abc.trial = 1
except:
new_abc.trial = np.array([1])
assert new_abc.trial == np.array([1])
def test_abc_build():
new_abc = abc.ABC()
assert new_abc.built == True
def test_abc_params():
params = {"n_trials": 5}
new_abc = abc.ABC(params=params)
assert new_abc.n_trials == 5
