def test_user_function_result_validation():
# 2d x
with pytest.raises(ValueError):
UserFunctionResult(np.array([[1]]), np.array([1]))
# 2d y
with pytest.raises(ValueError):
UserFunctionResult(np.array([1]), np.array([[1]]))
# 2d cost
with pytest.raises(ValueError):
UserFunctionResult(np.array([1]), np.array([1]), cost=np.array([[1]]))
def mock_user_function():
user_function_results = [UserFunctionResult(np.array([0]), np.array([0]))]
user_function = mock.create_autospec(UserFunction)
user_function.evaluate.return_value = user_function_results
return user_function
def test_loop():
n_iterations = 5
x_init = np.random.rand(5, 1)
y_init = np.random.rand(5, 1)
yc_init = np.random.rand(5, 1)
x = ContinuousParameter('x', 0, 1)
bo = UnknownConstraintGPBayesianOptimization(variables_list=[x],
X=x_init,
Y=y_init,
Yc=yc_init,
batch_size=1)
results = None
for _ in range(n_iterations + 1):
X_new = bo.get_next_points(results)
Y_new = f(X_new)
Yc_new = fc(X_new)
results = [
UserFunctionResult(X_new[0], Y_new[0], Y_constraint=Yc_new[0])
]
# Check we got the correct number of points
assert bo.loop_state.X.shape[0] == n_iterations + 5
def evaluate(self, X: np.ndarray):
res = []
for xi in X:
yi = self.b.objective_function(xi)["function_value"]
res.append(UserFunctionResult(xi, np.array([yi])))
return res
X_init = init_design.get_samples(args.n_init)
Y_init, C_init = evaluate(X_init)
model = BOGP(X_init=X_init, Y_init=Y_init)
acquisition = LogExpectedImprovement(model)
# acquisition_optimizer = DirectOptimizer(space)
acquisition_optimizer = DifferentialEvolution(space)
candidate_point_calculator = Sequential(acquisition, acquisition_optimizer)
bo = BayesianOptimizationLoop(model=model, space=space, X_init=X_init, Y_init=Y_init, acquisition=acquisition,
candidate_point_calculator=candidate_point_calculator)
initial_results = []
for i in range(X_init.shape[0]):
initial_results.append(UserFunctionResult(X_init[i], Y_init[i], C_init[i]))
loop_state = LoopState(initial_results)
bo.loop_state = loop_state
bo.run_loop(user_function=obj,
stopping_condition=FixedIterationsStoppingCondition(args.num_iterations - args.n_init))
C = bo.loop_state.C
incumbents = []
curr_inc = None
curr_inc_val = np.inf
for yi, xi in zip(bo.loop_state.Y, bo.loop_state.X):
if curr_inc_val > yi[0]:
curr_inc = xi
curr_inc_val = yi[0]
incumbents.append(curr_inc)