def posterior_to_xarray(self):
"""Convert the posterior to an xarray dataset."""
# Do not make pyro a requirement
from pyro.infer import EmpiricalMarginal
try: # Try pyro>=0.3 release syntax
data = {
name: utils.expand_dims(samples.enumerate_support().squeeze())
if self.posterior.num_chains == 1 else
samples.enumerate_support().squeeze()
for name, samples in self.posterior.marginal(
sites=self.latent_vars).empirical.items()
}
except AttributeError: # Use pyro<0.3 release syntax
data = {}
for var_name in self.latent_vars:
# pylint: disable=no-member
samples = EmpiricalMarginal(
self.posterior,
sites=var_name).get_samples_and_weights()[0]
samples = samples.numpy().squeeze()
data[var_name] = utils.expand_dims(samples)
return dict_to_dataset(data,
library=self.pyro,
coords=self.coords,
dims=self.dims)
def observed_data_to_xarray(self):
"""Convert observed data to xarray."""
from pyro.infer import EmpiricalMarginal
data = {}
for var_name in self.observed_vars:
samples = EmpiricalMarginal(
self.posterior, sites=var_name).get_samples_and_weights()[0]
data[var_name] = np.expand_dims(samples.numpy().squeeze(), 0)
return dict_to_dataset(data,
library=self.pyro,
coords=self.coords,
dims=self.dims)
def posterior_to_xarray(self):
"""Convert the posterior to an xarray dataset."""
# Do not make pyro a requirement
from pyro.infer import EmpiricalMarginal
data = {}
for var_name in self.latent_vars:
samples = EmpiricalMarginal(
self.posterior, sites=var_name).get_samples_and_weights()[0]
data[var_name] = np.expand_dims(samples.numpy().squeeze(), 0)
return dict_to_dataset(data,
library=self.pyro,
coords=self.coords,
dims=self.dims)
def observed_data_to_xarray(self):
"""Convert observed data to xarray."""
from pyro.infer import EmpiricalMarginal
try: # Try pyro>=0.3 release syntax
data = {
name: np.expand_dims(samples.enumerate_support(), 0)
for name, samples in self.posterior.marginal(
sites=self.observed_vars).empirical.items()
}
except AttributeError: # Use pyro<0.3 release syntax
data = {}
for var_name in self.observed_vars:
samples = EmpiricalMarginal(
self.posterior,
sites=var_name).get_samples_and_weights()[0]
data[var_name] = np.expand_dims(samples.numpy().squeeze(), 0)
return dict_to_dataset(data,
library=self.pyro,
coords=self.coords,
dims=self.dims)
