def test_mean_function_VSGP():
X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function()
Xu = X[::20].clone()
likelihood = Gaussian()
model = VariationalSparseGP(X, y, kernel, Xu, likelihood, mean_function=mean_fn)
model.optimize(optim.Adam({"lr": 0.02}))
_post_test_mean_function(model, Xnew, ynew)
def test_mean_function_VSGP():
X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function()
Xu = X[::20].clone()
likelihood = Gaussian()
gpmodule = VariationalSparseGP(X, y, kernel, Xu, likelihood, mean_function=mean_fn)
optimizer = torch.optim.Adam(gpmodule.parameters(), lr=0.02)
train(gpmodule, optimizer)
_post_test_mean_function(gpmodule, Xnew, ynew)
def test_mean_function_VSGP():
X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function()
Xu = X[::20].clone()
likelihood = Gaussian()
model = VariationalSparseGP(X,
y,
kernel,
Xu,
likelihood,
mean_function=mean_fn)
model.optimize(optim.Adam({"lr": 0.02}))
_post_test_mean_function(model, Xnew, ynew)
def test_inference_whiten_vsgp():
N = 1000
X = dist.Uniform(torch.zeros(N), torch.ones(N)*5).sample()
y = 0.5 * torch.sin(3*X) + dist.Normal(torch.zeros(N), torch.ones(N)*0.5).sample()
kernel = RBF(input_dim=1)
Xu = torch.arange(0, 5.5, 0.5)
vsgp = VariationalSparseGP(X, y, kernel, Xu, Gaussian(), whiten=True)
vsgp.optimize(optim.Adam({"lr": 0.01}), num_steps=1000)
Xnew = torch.arange(0, 5.05, 0.05)
loc, var = vsgp(Xnew, full_cov=False)
target = 0.5 * torch.sin(3*Xnew)
assert_equal((loc - target).abs().mean().item(), 0, prec=0.07)
def test_inference_deepGP():
gp1 = GPRegression(
X, None,
RBF(input_dim=3,
variance=torch.tensor(3.),
lengthscale=torch.tensor(2.)))
Z, _ = gp1.model()
gp2 = VariationalSparseGP(Z, y2D, Matern32(input_dim=3), Z.clone(),
Gaussian(torch.tensor(1e-6)))
class DeepGP(torch.nn.Module):
def __init__(self, gp1, gp2):
super(DeepGP, self).__init__()
self.gp1 = gp1
self.gp2 = gp2
def model(self):
Z, _ = self.gp1.model()
self.gp2.set_data(Z, y2D)
self.gp2.model()
def guide(self):
self.gp1.guide()
self.gp2.guide()
deepgp = DeepGP(gp1, gp2)
train(deepgp, num_steps=1)
def test_inference_vsgp():
N = 1000
X = dist.Uniform(torch.zeros(N), torch.ones(N)*5).sample()
y = 0.5 * torch.sin(3*X) + dist.Normal(torch.zeros(N), torch.ones(N)*0.5).sample()
kernel = RBF(input_dim=1)
Xu = torch.arange(0., 5.5, 0.5)
vsgp = VariationalSparseGP(X, y, kernel, Xu, Gaussian())
optimizer = torch.optim.Adam(vsgp.parameters(), lr=0.03)
train(vsgp, optimizer)
Xnew = torch.arange(0., 5.05, 0.05)
loc, var = vsgp(Xnew, full_cov=False)
target = 0.5 * torch.sin(3*Xnew)
assert_equal((loc - target).abs().mean().item(), 0, prec=0.06)
def test_inference_whiten_vsgp():
N = 1000
X = dist.Uniform(torch.zeros(N), torch.ones(N) * 5).sample()
y = 0.5 * torch.sin(3 * X) + dist.Normal(torch.zeros(N),
torch.ones(N) * 0.5).sample()
kernel = RBF(input_dim=1)
Xu = torch.arange(0, 5.5, 0.5)
vsgp = VariationalSparseGP(X, y, kernel, Xu, Gaussian(), whiten=True)
vsgp.optimize(optim.Adam({"lr": 0.01}), num_steps=1000)
Xnew = torch.arange(0, 5.05, 0.05)
loc, var = vsgp(Xnew, full_cov=False)
target = 0.5 * torch.sin(3 * Xnew)
assert_equal((loc - target).abs().mean().item(), 0, prec=0.07)
def test_inference_deepGP():
gp1 = GPRegression(X, None, kernel, name="GPR1")
Z, _ = gp1.model()
gp2 = VariationalSparseGP(Z,
y2D,
Matern32(input_dim=3),
Z.clone(),
likelihood,
name="GPR2")
def model():
Z, _ = gp1.model()
gp2.set_data(Z, y2D)
gp2.model()
def guide():
gp1.guide()
gp2.guide()
svi = SVI(model, guide, optim.Adam({}), Trace_ELBO())
svi.step()
