class TestGaussianProcess(unittest.TestCase):
def setUp(self):
self.X = np.random.rand(10, 2)
self.y = np.sinc(self.X * 10 - 5).sum(axis=1)
self.kernel = george.kernels.Matern52Kernel(np.ones(self.X.shape[1]),
ndim=self.X.shape[1])
prior = TophatPrior(-2, 2)
self.model = GaussianProcess(self.kernel,
prior=prior,
normalize_input=False,
normalize_output=False)
self.model.train(self.X, self.y, do_optimize=False)
def test_predict(self):
X_test = np.random.rand(10, 2)
m, v = self.model.predict(X_test)
assert len(m.shape) == 1
assert m.shape[0] == X_test.shape[0]
assert len(v.shape) == 1
assert v.shape[0] == X_test.shape[0]
m, v = self.model.predict(X_test, full_cov=True)
assert len(m.shape) == 1
assert m.shape[0] == X_test.shape[0]
assert len(v.shape) == 2
assert v.shape[0] == X_test.shape[0]
assert v.shape[1] == X_test.shape[0]
K_zz = self.kernel.value(X_test)
K_zx = self.kernel.value(X_test, self.X)
K_nz = self.kernel.value(
self.X) + self.model.noise * np.eye(self.X.shape[0])
inv = spla.inv(K_nz)
K_zz_x = K_zz - np.dot(K_zx, np.inner(inv, K_zx))
assert np.mean((K_zz_x - v)**2) < 10e-5
def test_sample_function(self):
X_test = np.random.rand(8, 2)
n_funcs = 3
funcs = self.model.sample_functions(X_test, n_funcs=n_funcs)
assert len(funcs.shape) == 2
assert funcs.shape[0] == n_funcs
assert funcs.shape[1] == X_test.shape[0]
def test_predict_variance(self):
x_test1 = np.random.rand(1, 2)
x_test2 = np.random.rand(10, 2)
var = self.model.predict_variance(x_test1, x_test2)
assert len(var.shape) == 2
assert var.shape[0] == x_test2.shape[0]
assert var.shape[1] == x_test1.shape[0]
def test_nll(self):
theta = np.array([0.2, 0.2, 0.001])
nll = self.model.nll(theta)
def test_optimize(self):
theta = self.model.optimize()
# Hyperparameters are 2 length scales + noise
assert theta.shape[0] == 3
def test_get_incumbent(self):
inc, inc_val = self.model.get_incumbent()
b = np.argmin(self.y)
np.testing.assert_almost_equal(inc, self.X[b], decimal=5)
assert inc_val == self.y[b]
class TestGaussianProcess(unittest.TestCase):
def setUp(self):
self.X = np.random.rand(10, 2)
self.y = np.sinc(self.X * 10 - 5).sum(axis=1)
self.kernel = george.kernels.Matern52Kernel(np.ones(self.X.shape[1]),
ndim=self.X.shape[1])
prior = TophatPrior(-2, 2)
self.model = GaussianProcess(self.kernel, prior=prior,
normalize_input=False,
normalize_output=False)
self.model.train(self.X, self.y, do_optimize=False)
def test_predict(self):
X_test = np.random.rand(10, 2)
m, v = self.model.predict(X_test)
assert len(m.shape) == 1
assert m.shape[0] == X_test.shape[0]
assert len(v.shape) == 1
assert v.shape[0] == X_test.shape[0]
m, v = self.model.predict(X_test, full_cov=True)
assert len(m.shape) == 1
assert m.shape[0] == X_test.shape[0]
assert len(v.shape) == 2
assert v.shape[0] == X_test.shape[0]
assert v.shape[1] == X_test.shape[0]
K_zz = self.kernel.value(X_test)
K_zx = self.kernel.value(X_test, self.X)
K_nz = self.kernel.value(self.X) + self.model.noise * np.eye(self.X.shape[0])
inv = spla.inv(K_nz)
K_zz_x = K_zz - np.dot(K_zx, np.inner(inv, K_zx))
assert np.mean((K_zz_x - v)**2) < 10e-5
def test_sample_function(self):
X_test = np.random.rand(8, 2)
n_funcs = 3
funcs = self.model.sample_functions(X_test, n_funcs=n_funcs)
assert len(funcs.shape) == 2
assert funcs.shape[0] == n_funcs
assert funcs.shape[1] == X_test.shape[0]
def test_predict_variance(self):
x_test1 = np.random.rand(1, 2)
x_test2 = np.random.rand(10, 2)
var = self.model.predict_variance(x_test1, x_test2)
assert len(var.shape) == 2
assert var.shape[0] == x_test2.shape[0]
assert var.shape[1] == x_test1.shape[0]
def test_nll(self):
theta = np.array([0.2, 0.2, 0.001])
nll = self.model.nll(theta)
def test_optimize(self):
theta = self.model.optimize()
# Hyperparameters are 2 length scales + noise
assert theta.shape[0] == 3
def test_get_incumbent(self):
inc, inc_val = self.model.get_incumbent()
b = np.argmin(self.y)
np.testing.assert_almost_equal(inc, self.X[b], decimal=5)
assert inc_val == self.y[b]
