def test_record_feedbacks():
opt = CMAESOptimizer(initial_params=np.zeros(2))
ctrl = Controller(environment=ObjectiveFunction(),
behavior_search=JustOptimizer(opt),
record_feedbacks=True, accumulate_feedbacks=False)
returns = ctrl.learn()
assert_array_equal(returns, ctrl.feedbacks_)
def test_missing_behavior_search():
ctrl = Controller(environment=ObjectiveFunction())
beh = DummyBehavior(initial_params=np.array([0.0, 0.0]))
beh.init(0, 2)
feedback = ctrl.episode_with(beh)
assert_equal(len(feedback), 1)
assert_less(feedback[0], ctrl.environment.get_maximum_feedback())
def test_record_inputs():
opt = CMAESOptimizer(initial_params=np.zeros(2))
ctrl = Controller(environment=ObjectiveFunction(),
behavior_search=JustOptimizer(opt),
record_inputs=True)
returns = ctrl.learn()
assert_equal(len(returns), 10)
assert_equal(np.array(ctrl.inputs_).shape, (10, 1, 2))
def test_pickle_controller():
names = ["CMAESOptimizer", "REPSOptimizer"]
optimizers = [CMAESOptimizer, REPSOptimizer]
for name, Optimizer in zip(names, optimizers):
opt = Optimizer(initial_params=np.zeros(2))
ctrl = Controller(environment=ObjectiveFunction(),
behavior_search=JustOptimizer(opt))
assert_pickle(name, ctrl)
def test_learn_controller_cmaes_sphere():
opt = CMAESOptimizer(initial_params=np.zeros(2), random_state=0)
ctrl = Controller(environment=ObjectiveFunction(random_state=0),
behavior_search=JustOptimizer(opt),
n_episodes=200)
returns = ctrl.learn()
dist_to_maximum = returns.max() - ctrl.environment.get_maximum_feedback()
assert_greater(dist_to_maximum, -1e-5)
def test_no_behavior_search_subclass():
class NoBehaviorSearch(object):
pass
assert_raises_regexp(
TypeError, "requires subclass of 'BehaviorSearch'",
Controller, environment=ObjectiveFunction(),
behavior_search=NoBehaviorSearch())
def test_set_context():
env = ObjectiveFunction(name="Sphere", n_params=2, random_state=0)
env.init()
cenv = ContextualObjectiveFunction(name="LinearContextualSphere",
n_params=2,
n_context_dims=1,
random_state=0)
env2 = SetContext(cenv, context=np.array([0.0]))
env2.init()
assert_equal(env.get_num_inputs(), env2.get_num_inputs())
assert_equal(env.get_num_outputs(), env2.get_num_outputs())
assert_almost_equal(env.get_maximum_feedback(),
env2.get_maximum_feedback())
params = np.zeros(2)
f = np.empty(2)
for i, e in enumerate([env, env2]):
e.reset()
e.set_inputs(params)
e.step_action()
out = np.empty(1)
e.get_outputs(out)
assert_true(e.is_evaluation_done())
f[i] = e.get_feedback()[:]
assert_false(e.is_behavior_learning_done())
assert_almost_equal(f[0], f[1])
def test_record_test_results():
opt = CMAESOptimizer(initial_params=np.zeros(2))
ctrl = Controller(environment=ObjectiveFunction(),
behavior_search=JustOptimizer(opt),
n_episodes_before_test=10, n_episodes=100)
ctrl.learn()
results = np.array(ctrl.test_results_)
assert_equal(results.shape[0], 10)
assert_true(np.all(results[:-1] <= results[1:]))
def test_controller_cmaes_sphere():
opt = CMAESOptimizer(initial_params=np.zeros(2))
ctrl = Controller(environment=ObjectiveFunction(),
behavior_search=JustOptimizer(opt))
returns = ctrl.learn()
assert_equal(len(returns), 10)
def test_learning_fails_with_missing_behavior_search():
controller = Controller(environment=ObjectiveFunction())
assert_raises_regexp(ValueError, "BehaviorSearch is required",
controller.learn)
""" ================== Objective Function ================== Plot an artificial objective function that can be used as a benchmark for black-box optimization. """ print(__doc__) import matplotlib.pyplot as plt from bolero.environment import ObjectiveFunction fig = plt.figure() env = ObjectiveFunction(random_state=0) env.plot(fig) plt.show()
def test_noncontextual_environment():
assert_raises_regexp(TypeError,
"requires contextual environment",
ContextualController,
environment=ObjectiveFunction())