def test_hmc(model_class, X, y, kernel, likelihood):
if model_class is SparseGPRegression or model_class is VariationalSparseGP:
gp = model_class(X, y, kernel, X, likelihood)
else:
gp = model_class(X, y, kernel, likelihood)
kernel.set_prior("variance",
dist.Uniform(torch.tensor(0.5), torch.tensor(1.5)))
kernel.set_prior("lengthscale",
dist.Uniform(torch.tensor(1.0), torch.tensor(3.0)))
hmc_kernel = HMC(gp.model, step_size=1)
mcmc_run = MCMC(hmc_kernel, num_samples=10)
post_trace = defaultdict(list)
for trace, _ in mcmc_run._traces():
variance_name = param_with_module_name(kernel.name, "variance")
post_trace["variance"].append(trace.nodes[variance_name]["value"])
lengthscale_name = param_with_module_name(kernel.name, "lengthscale")
post_trace["lengthscale"].append(
trace.nodes[lengthscale_name]["value"])
if model_class is VariationalGP:
f_name = param_with_module_name(gp.name, "f")
post_trace["f"].append(trace.nodes[f_name]["value"])
if model_class is VariationalSparseGP:
u_name = param_with_module_name(gp.name, "u")
post_trace["u"].append(trace.nodes[u_name]["value"])
for param in post_trace:
param_mean = torch.mean(torch.stack(post_trace[param]), 0)
logger.info("Posterior mean - {}".format(param))
logger.info(param_mean)
def run_pyro_nuts(data, pfile, n_samples, params):
# import model, transformed_data functions (if exists) from pyro module
model = import_by_string(pfile + ".model")
assert model is not None, "model couldn't be imported"
transformed_data = import_by_string(pfile + ".transformed_data")
if transformed_data is not None:
transformed_data(data)
nuts_kernel = NUTS(model, step_size=0.0855)
mcmc_run = MCMC(nuts_kernel,
num_samples=n_samples,
warmup_steps=int(n_samples / 2))
posteriors = {k: [] for k in params}
for trace, _ in mcmc_run._traces(data, params):
for k in posteriors:
posteriors[k].append(trace.nodes[k]['value'])
#posteriors["sigma"] = list(map(torch.exp, posteriors["log_sigma"]))
#del posteriors["log_sigma"]
posterior_means = {
k: torch.mean(torch.stack(posteriors[k]), 0)
for k in posteriors
}
bb()
return posterior_means
def test_hmc(model_class, X, y, kernel, likelihood):
if model_class is SparseGPRegression or model_class is VariationalSparseGP:
gp = model_class(X, y, kernel, X, likelihood)
else:
gp = model_class(X, y, kernel, likelihood)
kernel.set_prior("variance", dist.Uniform(torch.tensor(0.5), torch.tensor(1.5)))
kernel.set_prior("lengthscale", dist.Uniform(torch.tensor(1.0), torch.tensor(3.0)))
hmc_kernel = HMC(gp.model, step_size=1)
mcmc_run = MCMC(hmc_kernel, num_samples=10)
post_trace = defaultdict(list)
for trace, _ in mcmc_run._traces():
variance_name = param_with_module_name(kernel.name, "variance")
post_trace["variance"].append(trace.nodes[variance_name]["value"])
lengthscale_name = param_with_module_name(kernel.name, "lengthscale")
post_trace["lengthscale"].append(trace.nodes[lengthscale_name]["value"])
if model_class is VariationalGP:
f_name = param_with_module_name(gp.name, "f")
post_trace["f"].append(trace.nodes[f_name]["value"])
if model_class is VariationalSparseGP:
u_name = param_with_module_name(gp.name, "u")
post_trace["u"].append(trace.nodes[u_name]["value"])
for param in post_trace:
param_mean = torch.mean(torch.stack(post_trace[param]), 0)
logger.info("Posterior mean - {}".format(param))
logger.info(param_mean)
