def test_pyro_sampling(self):
try:
import pyro # noqa
from pyro.infer.mcmc import NUTS, MCMC
except ImportError:
return
train_x, test_x, train_y, test_y = self._get_data(cuda=False)
likelihood = GaussianLikelihood(
noise_constraint=gpytorch.constraints.Positive())
gp_model = ExactGPModel(train_x, train_y, likelihood)
# Register normal GPyTorch priors
gp_model.mean_module.register_prior("mean_prior", UniformPrior(-1, 1),
"constant")
gp_model.covar_module.base_kernel.register_prior(
"lengthscale_prior", UniformPrior(0.01, 0.5), "lengthscale")
gp_model.covar_module.register_prior("outputscale_prior",
UniformPrior(1, 2), "outputscale")
likelihood.register_prior("noise_prior", UniformPrior(0.05, 0.3),
"noise")
def pyro_model(x, y):
with gpytorch.settings.fast_computations(False, False, False):
sampled_model = gp_model.pyro_sample_from_prior()
output = sampled_model.likelihood(sampled_model(x))
pyro.sample("obs", output, obs=y)
return y
nuts_kernel = NUTS(pyro_model, adapt_step_size=True)
mcmc_run = MCMC(nuts_kernel,
num_samples=3,
warmup_steps=20,
disable_progbar=True)
mcmc_run.run(train_x, train_y)
gp_model.pyro_load_from_samples(mcmc_run.get_samples())
gp_model.eval()
expanded_test_x = test_x.unsqueeze(-1).repeat(3, 1, 1)
output = gp_model(expanded_test_x)
self.assertEqual(output.mean.size(0), 3)
# All 3 samples should do reasonably well on a noiseless dataset.
self.assertLess(
torch.norm(output.mean[0] - test_y) / test_y.norm(), 0.2)
self.assertLess(
torch.norm(output.mean[1] - test_y) / test_y.norm(), 0.2)
self.assertLess(
torch.norm(output.mean[2] - test_y) / test_y.norm(), 0.2)
def test_pyro_sampling(self):
try:
import pyro
from pyro.infer.mcmc import NUTS, MCMC
except:
return
train_x, test_x, train_y, test_y = self._get_data(cuda=False)
likelihood = GaussianLikelihood(
noise_constraint=gpytorch.constraints.Positive())
gp_model = ExactGPModel(train_x, train_y, likelihood)
# Register normal GPyTorch priors
gp_model.mean_module.register_prior("mean_prior", UniformPrior(-1, 1),
"constant")
gp_model.covar_module.base_kernel.register_prior(
"lengthscale_prior", UniformPrior(0.01, 0.2), "lengthscale")
gp_model.covar_module.register_prior("outputscale_prior",
UniformPrior(1, 2), "outputscale")
likelihood.register_prior("noise_prior", LogNormalPrior(-1.5, 0.1),
"noise")
mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, gp_model)
def pyro_model(x, y):
gp_model.pyro_sample_from_prior()
output = gp_model(x)
loss = mll.pyro_factor(output, y)
return y
nuts_kernel = NUTS(pyro_model, adapt_step_size=True)
mcmc_run = MCMC(nuts_kernel, num_samples=3, warmup_steps=20)
mcmc_run.run(train_x, train_y)
gp_model.pyro_load_from_samples(mcmc_run.get_samples())
gp_model.eval()
expanded_test_x = test_x.unsqueeze(-1).repeat(3, 1, 1)
output = gp_model(expanded_test_x)
self.assertEqual(output.mean.size(0), 3)
# All 3 samples should do reasonably well on a noiseless dataset.
self.assertLess(
torch.norm(output.mean[0] - test_y) / test_y.norm(), 0.2)
self.assertLess(
torch.norm(output.mean[1] - test_y) / test_y.norm(), 0.2)
self.assertLess(
torch.norm(output.mean[2] - test_y) / test_y.norm(), 0.2)