class TestTask(BaseTask):
def __init__(self):
self.original_task = SinFunction()
# Add an additional dimension for the system size
X_lower = np.concatenate((self.original_task.original_X_lower,
np.array([0])))
X_upper = np.concatenate((self.original_task.original_X_upper,
np.array([1])))
self.is_env = np.zeros([self.original_task.n_dims])
self.is_env = np.concatenate((self.is_env, np.ones([1])))
super(TestTask, self).__init__(X_lower, X_upper)
def objective_function(self, x):
y = self.original_task.objective_function(x[:, self.is_env == 0]) \
* np.exp(-(x[:, np.newaxis, -1] - 1))
cost = np.exp(x[:, self.is_env == 1])
return y, cost
def objective_function_test(self, x):
return self.original_task.objective_function(x[:, self.is_env == 0])
class TestTask(BaseTask):
def __init__(self):
self.original_task = SinFunction()
# Add an additional dimension for the system size
X_lower = np.concatenate(
(self.original_task.original_X_lower, np.array([0])))
X_upper = np.concatenate(
(self.original_task.original_X_upper, np.array([1])))
self.is_env = np.zeros([self.original_task.n_dims])
self.is_env = np.concatenate((self.is_env, np.ones([1])))
super(TestTask, self).__init__(X_lower, X_upper)
def objective_function(self, x):
y = self.original_task.objective_function(x[:, self.is_env == 0]) \
* np.exp(-(x[:, np.newaxis, -1] - 1))
cost = np.exp(x[:, self.is_env == 1])
return y, cost
def objective_function_test(self, x):
return self.original_task.objective_function(x[:, self.is_env == 0])
def __init__(self):
self.original_task = SinFunction()
# Add an additional dimension for the system size
X_lower = np.concatenate(
(self.original_task.original_X_lower, np.array([0])))
X_upper = np.concatenate(
(self.original_task.original_X_upper, np.array([1])))
self.is_env = np.zeros([self.original_task.n_dims])
self.is_env = np.concatenate((self.is_env, np.ones([1])))
super(TestTask, self).__init__(X_lower, X_upper)
def setUp(self):
self.task = SinFunction()
kernel = george.kernels.Matern52Kernel(np.ones([self.task.n_dims]) * 0.01,
ndim=self.task.n_dims)
noise_kernel = george.kernels.WhiteKernel(1e-9, ndim=self.task.n_dims)
kernel = 3000 * (kernel + noise_kernel)
prior = default_priors.TophatPrior(-2, 2)
model = GaussianProcess(kernel, prior=prior)
X = init_random_uniform(self.task.X_lower, self.task.X_upper, 3)
Y = self.task.evaluate(X)
model.train(X, Y, do_optimize=False)
self.acquisition_func = InformationGainMC(model,
X_upper=self.task.X_upper,
X_lower=self.task.X_lower)
self.acquisition_func.update(model)
def __init__(self):
self.original_task = SinFunction()
# Add an additional dimension for the system size
X_lower = np.concatenate((self.original_task.original_X_lower,
np.array([0])))
X_upper = np.concatenate((self.original_task.original_X_upper,
np.array([1])))
self.is_env = np.zeros([self.original_task.n_dims])
self.is_env = np.concatenate((self.is_env, np.ones([1])))
super(TestTask, self).__init__(X_lower, X_upper)
class InformationGainMCTestCase(unittest.TestCase):
def setUp(self):
self.task = SinFunction()
kernel = george.kernels.Matern52Kernel(np.ones([self.task.n_dims]) * 0.01,
ndim=self.task.n_dims)
noise_kernel = george.kernels.WhiteKernel(1e-9, ndim=self.task.n_dims)
kernel = 3000 * (kernel + noise_kernel)
prior = default_priors.TophatPrior(-2, 2)
model = GaussianProcess(kernel, prior=prior)
X = init_random_uniform(self.task.X_lower, self.task.X_upper, 3)
Y = self.task.evaluate(X)
model.train(X, Y, do_optimize=False)
self.acquisition_func = InformationGainMC(model,
X_upper=self.task.X_upper,
X_lower=self.task.X_lower)
self.acquisition_func.update(model)
def test_sampling_representer_points(self):
# Check if representer points are inside the bounds
assert np.any(self.acquisition_func.zb >= self.acquisition_func.X_lower)
assert np.any(self.acquisition_func.zb <= self.acquisition_func.X_upper)
def test_compute_pmin(self):
# Uniform distribution
m = np.ones([self.acquisition_func.Nb, 1])
v = np.eye(self.acquisition_func.Nb)
pmin = mc_part.joint_pmin(m, v, self.acquisition_func.Nf)
uprob = 1. / self.acquisition_func.Nb
assert pmin.shape[0] == self.acquisition_func.Nb
assert np.any(pmin < (uprob + 0.03)) and np.any(pmin > uprob - 0.01)
# Dirac delta
m = np.ones([self.acquisition_func.Nb, 1]) * 1000
m[0] = 1
v = np.eye(self.acquisition_func.Nb)
pmin = mc_part.joint_pmin(m, v, self.acquisition_func.Nf)
uprob = 1. / self.acquisition_func.Nb
assert pmin[0] == 1.0
assert np.any(pmin[:1] > 1e-10)
# Check uniform case with halluzinated values
m = np.ones([self.acquisition_func.Nb, 50]) * 1000
for i in range(50):
m[i, i] = 1
v = np.eye(self.acquisition_func.Nb)
pmin = mc_part.joint_pmin(m, v, self.acquisition_func.Nf)
assert pmin.shape[0] == self.acquisition_func.Nb
assert np.any(pmin < (uprob + 0.03)) and np.any(pmin > uprob - 0.01)
def test_innovations(self):
# Case 1: Assume no influence of test point on representer points
rep = np.array([[1.0]])
x = np.array([[0.0]])
dm, dv = self.acquisition_func.innovations(x, rep)
assert np.any(np.abs(dm) < 1e-4)
assert np.any(np.abs(dv) < 1e-4)
# Case 2: Test point is close to representer points
rep = np.array([[1.0]])
x = np.array([[0.99]])
dm, dv = self.acquisition_func.innovations(x, rep)
assert np.any(np.abs(dm) > 1e-4)
assert np.any(np.abs(dv) > 1e-4)
def test_general_interface(self):
X_test = init_random_uniform(self.task.X_lower, self.task.X_upper, 1)
a = self.acquisition_func(X_test, False)
assert len(a.shape) == 2
assert a.shape[0] == X_test.shape[0]
assert a.shape[1] == 1
