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_batch_experimental_design_loop():
class MockGPyModel(GPyModelWrapper):
def optimize(self):
# speed up test by skipping the actual hyper-parameter optimization
pass
user_function = lambda x: x
space = ParameterSpace([ContinuousParameter("x", 0, 3)])
# Make model
x_init = np.linspace(0, 3, 5)[:, None]
y_init = user_function(x_init)
gpy_model = GPy.models.GPRegression(x_init, y_init)
model = MockGPyModel(gpy_model)
loop = ExperimentalDesignLoop(space, model, batch_size=5)
loop.run_loop(user_function, 5)
assert loop.loop_state.iteration == 5
assert loop.loop_state.X.shape[0] == 30
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 test_non_integer_batch_size(mock_model):
space = ParameterSpace([ContinuousParameter("x", 0, 3)])
# Make model
with pytest.raises(ValueError):
ExperimentalDesignLoop(space, mock_model, batch_size=3.5)
def test_zero_batch_size(mock_model):
space = ParameterSpace([ContinuousParameter('x', 0, 3)])
# Make model
with pytest.raises(ValueError):
ExperimentalDesignLoop(space, mock_model, batch_size=0)