def test_iso_gauss_measure_invalid_input():
wrong_variance = -2.
mean_wrong_dim = np.ones([3, 1])
mean_wrong_type = 0.
with pytest.raises(ValueError):
IsotropicGaussianMeasure(mean=np.ones(3), variance=wrong_variance)
with pytest.raises(TypeError):
IsotropicGaussianMeasure(mean=mean_wrong_type, variance=1.)
with pytest.raises(ValueError):
IsotropicGaussianMeasure(mean=mean_wrong_dim, variance=1.)
def test_iso_gauss_measure_shapes():
D = 4
N = 5
x = np.reshape(np.random.randn(D * N), [N, D])
measure = IsotropicGaussianMeasure(mean=np.ones(D), variance=1.)
bounds = measure.get_box()
assert len(bounds) == D
assert len(bounds[0]) == 2
assert measure.dim == D
res = measure.compute_density(x)
assert res.shape == (N, )
def test_iso_gauss_measure_gradients():
D = 2
measure = IsotropicGaussianMeasure(mean=np.random.randn(D),
variance=np.random.randn()**2)
N = 3
x = np.reshape(np.random.randn(D * N), [N, D])
_check_grad(measure, x)
def test_iso_gauss_measure_shapes():
D = 4
N = 5
x = np.reshape(np.random.randn(D * N), [N, D])
measure = IsotropicGaussianMeasure(mean=np.ones(D), variance=1.)
bounds = measure.get_box()
assert len(bounds) == D
assert len(bounds[0]) == 2
assert measure.num_dimensions == D
res = measure.compute_density(x)
assert res.shape == (N, )
# sampling capabilities
assert measure.can_sample
res = measure.get_samples(N)
assert res.shape == (N, D)
def base_gp_data():
X = np.array([[-1, 1], [0, 0], [-2, 0.1]])
Y = np.array([[1], [2], [3]])
gpy_model = GPy.models.GPRegression(
X=X, Y=Y, kernel=GPy.kern.RBF(input_dim=X.shape[1]))
measure = IsotropicGaussianMeasure(mean=np.array([0.1, 1.8]), variance=0.8)
qrbf = QuadratureRBFIsoGaussMeasure(RBFGPy(gpy_model.kern),
measure=measure)
base_gp = BaseGaussianProcessGPy(kern=qrbf, gpy_model=gpy_model)
return base_gp, X, Y
def model_with_density():
rng = np.random.RandomState(42)
x_init = rng.rand(5, 2)
y_init = rng.rand(5, 1)
gpy_kernel = GPy.kern.RBF(input_dim=x_init.shape[1])
gpy_model = GPy.models.GPRegression(X=x_init, Y=y_init, kernel=gpy_kernel)
measure = IsotropicGaussianMeasure(mean=np.arange(x_init.shape[1]), variance=2.)
qrbf = QuadratureRBFIsoGaussMeasure(RBFGPy(gpy_kernel), measure=measure)
basegp = BaseGaussianProcessGPy(kern=qrbf, gpy_model=gpy_model)
model = VanillaBayesianQuadrature(base_gp=basegp, X=x_init, Y=y_init)
return model
def qrbf_iso_gauss_infinite():
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]
gpy_kernel = GPy.kern.RBF(input_dim=D)
emukit_rbf = RBFGPy(gpy_kernel)
measure = IsotropicGaussianMeasure(mean=np.arange(D), variance=2.)
emukit_qrbf = QuadratureRBFIsoGaussMeasure(rbf_kernel=emukit_rbf,
measure=measure)
return emukit_qrbf, x1, x2, M1, M2, D
MEASURE_INTBOUNDS = "Uniform-finite"
# === CHOOSE MEASURE ABOVE ======
x1 = np.array([[-1, 1], [0, 0], [-2, 0.1]])
x2 = np.array([[-1, 1], [0, 0], [-2, 0.1], [-3, 3]])
D = x1.shape[1]
gpy_kernel = GPy.kern.RBF(input_dim=D)
emukit_rbf = RBFGPy(gpy_kernel)
if MEASURE_INTBOUNDS == "Lebesgue-finite":
emukit_qrbf = QuadratureRBFLebesgueMeasure(emukit_rbf,
integral_bounds=[(-1, 2),
(-3, 3)])
elif MEASURE_INTBOUNDS == "GaussIso-infinite":
measure = IsotropicGaussianMeasure(mean=np.arange(D), variance=2.0)
emukit_qrbf = QuadratureRBFIsoGaussMeasure(rbf_kernel=emukit_rbf,
measure=measure)
elif MEASURE_INTBOUNDS == "Uniform-infinite":
measure = UniformMeasure(bounds=[(0, 2), (-4, 3)])
emukit_qrbf = QuadratureRBFUniformMeasure(emukit_rbf,
integral_bounds=None,
measure=measure)
elif MEASURE_INTBOUNDS == "Uniform-finite":
measure = UniformMeasure(bounds=[(1, 2), (-4, 2)])
emukit_qrbf = QuadratureRBFUniformMeasure(emukit_rbf,
integral_bounds=[(-1, 2),
(-3, 3)],
measure=measure)
else:
raise ValueError("Measure-integral-bounds combination not defined")
# choose model here:
# METHOD = 'Bounded BQ lower'
# METHOD = 'Bounded BQ upper'
# METHOD = 'WSABI-l adapt'
METHOD = "WSABI-l fixed"
# the GPy model
X = np.array([[-1, 1], [0, 0], [-2, 0.1]])
Y = np.array([[1], [2], [3]])
D = X.shape[1]
gpy_model = GPy.models.GPRegression(X=X,
Y=Y,
kernel=GPy.kern.RBF(input_dim=D))
# the measure
measure = IsotropicGaussianMeasure(mean=np.array([0.1, 1.8]), variance=0.8)
# the emukit base GP
qrbf = QuadratureRBFIsoGaussMeasure(RBFGPy(gpy_model.kern),
measure=measure)
base_gp = BaseGaussianProcessGPy(kern=qrbf, gpy_model=gpy_model)
# the emukit bounded BQ model
if METHOD == "Bounded BQ lower":
bound = np.min(base_gp.Y) - 0.5
model = BoundedBayesianQuadrature(base_gp=base_gp,
X=X,
Y=Y,
bound=bound,
is_lower_bounded=True)
elif METHOD == "Bounded BQ upper":
def base_gp(gpy_model):
measure = IsotropicGaussianMeasure(mean=np.array([0.1, 1.8]), variance=0.8)
qrbf = QuadratureRBFIsoGaussMeasure(RBFGPy(gpy_model.kern),
measure=measure)
return BaseGaussianProcessGPy(kern=qrbf, gpy_model=gpy_model)