def test_loop():
n_iterations = 5
x_init = np.random.rand(5, 1)
y_init = np.random.rand(5, 1)
# Make GPy model
gpy_model = GPy.models.GPRegression(x_init, y_init)
model = GPyModelWrapper(gpy_model)
space = ParameterSpace([ContinuousParameter('x', 0, 1)])
acquisition = ExpectedImprovement(model)
# Make loop and collect points
bo = BayesianOptimizationLoop(model=model,
space=space,
acquisition=acquisition)
bo.run_loop(UserFunctionWrapper(f),
FixedIterationsStoppingCondition(n_iterations))
# Check we got the correct number of points
assert bo.loop_state.X.shape[0] == n_iterations + 5
# Check the obtained results
results = bo.get_results()
assert results.minimum_location.shape[0] == 1
assert results.best_found_value_per_iteration.shape[0] == n_iterations + 5
def test_loop():
n_iterations = 5
x_init = np.random.rand(5, 1)
y_init = np.random.rand(5, 1)
x = ContinuousParameter('x', 0, 1)
bo = GPBayesianOptimization(variables_list=[x], X=x_init, Y=y_init)
bo.run_optimization(f, n_iterations)
# Check we got the correct number of points
assert bo.loop_state.X.shape[0] == n_iterations + 5
def test_batch_loop_fails_without_gradients_implemented():
parameter_space = ParameterSpace([ContinuousParameter('x', 0, 1)])
model = mock.create_autospec(IModel)
base_acquisition = ExpectedImprovement(model)
batch_size = 10
with pytest.raises(ValueError):
BayesianOptimizationLoop(parameter_space, model, base_acquisition,
batch_size)
def test_loop_initial_state():
x_init = np.random.rand(5, 1)
y_init = np.random.rand(5, 1)
gpy_model = GPy.models.GPRegression(x_init, y_init)
model = GPyModelWrapper(gpy_model)
space = ParameterSpace([ContinuousParameter('x', 0, 1)])
exp_design = ExperimentalDesignLoop(space, model)
# test loop state initialization
assert_array_equal(exp_design.loop_state.X, x_init)
assert_array_equal(exp_design.loop_state.Y, y_init)
def convert_to_list_of_continuous_parameters(
self) -> List[ContinuousParameter]:
"""
converts the integral bounds into a list of ContinuousParameter objects
:return: a list if ContinuousParameter objects (one for each dimension)
"""
continuous_parameters = []
for i, bounds_d in enumerate(self.bounds):
lb_d, ub_d = bounds_d
name_d = self.name + '_' + str(i)
param = ContinuousParameter(name=name_d,
min_value=lb_d,
max_value=ub_d)
continuous_parameters.append(param)
return continuous_parameters
def test_loop():
n_iterations = 5
x_init = np.random.rand(5, 1)
y_init = np.random.rand(5, 1)
# Make GPy model
gpy_model = GPy.models.GPRegression(x_init, y_init)
model = GPyModelWrapper(gpy_model)
space = ParameterSpace([ContinuousParameter('x', 0, 1)])
acquisition = ModelVariance(model)
# Make loop and collect points
exp_design = ExperimentalDesignLoop(space, model, acquisition)
exp_design.run_loop(UserFunctionWrapper(f), FixedIterationsStoppingCondition(n_iterations))
# Check we got the correct number of points
assert exp_design.loop_state.X.shape[0] == 10
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