def test_vanilla_bq_shapes():
X = np.array([[-1, 1], [0, 0], [-2, 0.1]])
Y = np.array([[1], [2], [3]])
x = np.array([[-1, 1], [0, 0], [-2, 0.1], [-3, 4]])
D = X.shape[1]
# integral bounds
lb = -3
ub = 3
gpy_model = GPy.models.GPRegression(X=X,
Y=Y,
kernel=GPy.kern.RBF(input_dim=D))
emukit_qrbf = QuadratureRBF(RBFGPy(gpy_model.kern),
integral_bounds=D * [(lb, ub)])
emukit_model = BaseGaussianProcessGPy(kern=emukit_qrbf,
gpy_model=gpy_model)
vanilla_bq = VanillaBayesianQuadrature(base_gp=emukit_model)
# integrate
res = vanilla_bq.integrate()
assert len(res) == 2
assert isinstance(res[0], float)
assert isinstance(res[1], float)
# transformations
assert vanilla_bq.transform(Y).shape == Y.shape
assert vanilla_bq.inverse_transform(Y).shape == Y.shape
# predictions base
res = vanilla_bq.predict_base(x)
assert len(res) == 4
for i in range(4):
assert res[i].shape == (x.shape[0], 1)
# predictions base full covariance
res = vanilla_bq.predict_base_with_full_covariance(x)
assert len(res) == 4
assert res[0].shape == (x.shape[0], 1)
assert res[1].shape == (x.shape[0], x.shape[0])
assert res[2].shape == (x.shape[0], 1)
assert res[3].shape == (x.shape[0], x.shape[0])
# predictions
res = vanilla_bq.predict(x)
assert len(res) == 2
assert res[0].shape == (x.shape[0], 1)
assert res[1].shape == (x.shape[0], 1)
# predictions full covariance
res = vanilla_bq.predict_with_full_covariance(x)
assert len(res) == 2
assert res[0].shape == (x.shape[0], 1)
assert res[1].shape == (x.shape[0], x.shape[0])
def _wrap_emukit(self, gpy_gp: GPy.core.GP):
"""
Wrap GPy GP around Emukit interface to enable subsequent quadrature
:param gpy_gp:
:return:
"""
# gpy_gp.optimize()
rbf = RBFGPy(gpy_gp.kern)
qrbf = QuadratureRBF(rbf, integral_bounds=[(-4, 8)] * self.dimensions)
model = BaseGaussianProcessGPy(kern=qrbf, gpy_model=gpy_gp)
method = VanillaBayesianQuadrature(base_gp=model)
return method
def vanilla_bq():
X = np.array([[-1, 1], [0, 0], [-2, 0.1]])
Y = np.array([[1], [2], [3]])
D = X.shape[1]
integral_bounds = D * [(-3, 3)]
gpy_model = GPy.models.GPRegression(X=X,
Y=Y,
kernel=GPy.kern.RBF(input_dim=D))
qrbf = QuadratureRBF(RBFGPy(gpy_model.kern),
integral_bounds=integral_bounds)
model = BaseGaussianProcessGPy(kern=qrbf, gpy_model=gpy_model)
vanilla_bq = VanillaBayesianQuadrature(base_gp=model)
return vanilla_bq
def test_rbf_qkernel_shapes():
x1 = np.array([[-1, 1], [0, 0], [-2, 0.1]])
x2 = np.array([[-1, 1], [0, 0], [-2, 0.1], [-3, 3]])
M1 = x1.shape[0]
M2 = x2.shape[0]
D = x1.shape[1]
# integral bounds
lb = -3
ub = 3
gpy_kernel = GPy.kern.RBF(input_dim=D)
emukit_rbf = RBFGPy(gpy_kernel)
emukit_qrbf = QuadratureRBF(emukit_rbf, integral_bounds=D * [(lb, ub)])
# kernel shapes
assert emukit_qrbf.K(x1, x2).shape == (M1, M2)
assert emukit_qrbf.qK(x2).shape == (1, M2)
assert emukit_qrbf.Kq(x1).shape == (M1, 1)
assert isinstance(emukit_qrbf.qKq(), float)
# gradient shapes
assert emukit_qrbf.dKq_dx(x1).shape == (M1, D)
assert emukit_qrbf.dqK_dx(x2).shape == (D, M2)
def test_rbf_qkernel_shapes():
x1 = np.array([[-1, 1], [0, 0], [-2, 0.1]])
x2 = np.array([[-1, 1], [0, 0], [-2, 0.1], [-3, 3]])
M1 = x1.shape[0]
M2 = x2.shape[0]
D = x1.shape[1]
# integral bounds
lb = -3
ub = 3
gpy_kernel = GPy.kern.RBF(input_dim=D)
emukit_rbf = RBFGPy(gpy_kernel)
emukit_qrbf = QuadratureRBF(emukit_rbf, integral_bounds=D * [(lb, ub)])
# kernel shapes
assert emukit_qrbf.K(x1, x2).shape == (M1, M2)
assert emukit_qrbf.qK(x2).shape == (1, M2)
assert emukit_qrbf.Kq(x1).shape == (M1, 1)
assert isinstance(emukit_qrbf.qKq(), float)
# gradient shapes
assert emukit_qrbf.dKq_dx(x1).shape == (M1, D)
assert emukit_qrbf.dqK_dx(x2).shape == (D, M2)