def test_ep_params_setter():
new_ep = ep.EP()
try:
new_ep.bout_size = "a"
except:
new_ep.bout_size = 0.5
try:
new_ep.bout_size = -1
except:
new_ep.bout_size = 0.5
assert new_ep.bout_size == 0.5
try:
new_ep.clip_ratio = "b"
except:
new_ep.clip_ratio = 0.5
try:
new_ep.clip_ratio = -1
except:
new_ep.clip_ratio = 0.5
assert new_ep.clip_ratio == 0.5
def test_ep_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
hyperparams = {'bout_size': 0.1, 'clip_ratio': 0.05}
new_ep = ep.EP(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_ep.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 de failed to converge.'
def test_ep_hyperparams_setter():
new_ep = ep.EP()
try:
new_ep.bout_size = 'a'
except:
new_ep.bout_size = 0.5
try:
new_ep.bout_size = -1
except:
new_ep.bout_size = 0.5
assert new_ep.bout_size == 0.5
try:
new_ep.clip_ratio = 'b'
except:
new_ep.clip_ratio = 0.5
try:
new_ep.clip_ratio = -1
except:
new_ep.clip_ratio = 0.5
assert new_ep.clip_ratio == 0.5
def test_ep_hyperparams():
hyperparams = {'bout_size': 0.1, 'clip_ratio': 0.05}
new_ep = ep.EP(hyperparams=hyperparams)
assert new_ep.bout_size == 0.1
assert new_ep.clip_ratio == 0.05
def test_ep_update_strategy():
new_ep = ep.EP()
strategy = np.zeros((4, 1))
new_strategy = new_ep._update_strategy(strategy, [1], [2])
assert new_strategy[0][0] > 0
def test_ep_params():
params = {"bout_size": 0.1, "clip_ratio": 0.05}
new_ep = ep.EP(params=params)
assert new_ep.bout_size == 0.1
assert new_ep.clip_ratio == 0.05
def test_ep_update_strategy():
search_space = search.SearchSpace(n_agents=10, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_ep = ep.EP()
new_ep.compile(search_space)
new_ep._update_strategy(0, [1], [2])
assert new_ep.strategy[0][0] > 0
def test_ep_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_ep = ep.EP()
new_ep.compile(search_space)
new_ep.update(search_space, square)
def test_ep_params():
params = {
'bout_size': 0.1,
'clip_ratio': 0.05
}
new_ep = ep.EP(params=params)
assert new_ep.bout_size == 0.1
assert new_ep.clip_ratio == 0.05
def test_ep_mutate_parent():
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_ep = ep.EP()
new_ep.compile(search_space)
agent = new_ep._mutate_parent(search_space.agents[0], 0, square)
assert agent.position[0][0] > 0
def test_ep_compile():
search_space = search.SearchSpace(n_agents=10, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_ep = ep.EP()
new_ep.compile(search_space)
try:
new_ep.strategy = 1
except:
new_ep.strategy = np.array([1])
assert new_ep.strategy == np.array([1])
def test_ep_mutate_parent():
def square(x):
return np.sum(x**2)
new_ep = ep.EP()
search_space = search.SearchSpace(n_agents=4, n_iterations=100,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
strategy = np.zeros(4)
agent = new_ep._mutate_parent(search_space.agents[0], square, strategy[0])
assert agent.position[0][0] > 0
def test_ep_build():
new_ep = ep.EP()
assert new_ep.built == True
