def test_null_model_with_hook(run_mcmc_cls, kernel, model, jit, num_chains):
num_warmup, num_samples = 10, 10
initial_params, potential_fn, transforms, _ = initialize_model(
model, num_chains=num_chains)
iters = []
hook = partial(_hook, iters)
mp_context = "spawn" if "CUDA_TEST" in os.environ else None
kern = kernel(potential_fn=potential_fn,
transforms=transforms,
jit_compile=jit)
samples, _ = run_mcmc_cls(
data=None,
kernel=kern,
num_samples=num_samples,
warmup_steps=num_warmup,
initial_params=initial_params,
hook_fn=hook,
num_chains=num_chains,
mp_context=mp_context,
)
assert samples == {}
if num_chains == 1:
expected = [("Warmup", i)
for i in range(num_warmup)] + [("Sample", i)
for i in range(num_samples)]
assert iters == expected
def test_mcmc_interface(num_draws, group_by_chain, num_chains):
num_samples = 2000
data = torch.tensor([1.0])
initial_params, _, transforms, _ = initialize_model(normal_normal_model, model_args=(data,),
num_chains=num_chains)
kernel = PriorKernel(normal_normal_model)
mcmc = MCMC(kernel=kernel, num_samples=num_samples, warmup_steps=100, num_chains=num_chains,
mp_context="spawn", initial_params=initial_params, transforms=transforms)
mcmc.run(data)
samples = mcmc.get_samples(num_draws, group_by_chain=group_by_chain)
# test sample shape
expected_samples = num_draws if num_draws is not None else num_samples
if group_by_chain:
expected_shape = (mcmc.num_chains, expected_samples, 1)
elif num_draws is not None:
# FIXME: what is the expected behavior of num_draw is not None and group_by_chain=False?
expected_shape = (expected_samples, 1)
else:
expected_shape = (mcmc.num_chains * expected_samples, 1)
assert samples['y'].shape == expected_shape
# test sample stats
if group_by_chain:
samples = {k: v.reshape((-1,) + v.shape[2:]) for k, v in samples.items()}
sample_mean = samples['y'].mean()
sample_std = samples['y'].std()
assert_close(sample_mean, torch.tensor(0.0), atol=0.05)
assert_close(sample_std, torch.tensor(1.0), atol=0.05)
def test_num_chains(num_chains, cpu_count, default_init_params, monkeypatch):
monkeypatch.setattr(torch.multiprocessing, "cpu_count", lambda: cpu_count)
data = torch.tensor([1.0])
initial_params, _, transforms, _ = initialize_model(normal_normal_model,
model_args=(data, ),
num_chains=num_chains)
if default_init_params:
initial_params = None
kernel = PriorKernel(normal_normal_model)
available_cpu = max(1, cpu_count - 1)
mp_context = "spawn"
with optional(pytest.warns(UserWarning), available_cpu < num_chains):
mcmc = MCMC(
kernel,
num_samples=10,
warmup_steps=10,
num_chains=num_chains,
initial_params=initial_params,
transforms=transforms,
mp_context=mp_context,
)
mcmc.run(data)
assert mcmc.num_chains == num_chains
if mcmc.num_chains == 1 or available_cpu < num_chains:
assert isinstance(mcmc.sampler, _UnarySampler)
else:
assert isinstance(mcmc.sampler, _MultiSampler)
def test_predictive(num_samples, parallel):
model, data, true_probs = beta_bernoulli()
init_params, potential_fn, transforms, _ = initialize_model(
model, model_args=(data, ))
nuts_kernel = NUTS(potential_fn=potential_fn, transforms=transforms)
mcmc = MCMC(nuts_kernel, 100, initial_params=init_params, warmup_steps=100)
mcmc.run(data)
samples = mcmc.get_samples()
with ignore_experimental_warning():
with optional(pytest.warns(UserWarning), num_samples
not in (None, 100)):
predictive_samples = predictive(model,
samples,
num_samples=num_samples,
return_sites=["beta", "obs"],
parallel=parallel)
# check shapes
assert predictive_samples["beta"].shape == (100, 5)
assert predictive_samples["obs"].shape == (100, 1000, 5)
# check sample mean
assert_close(predictive_samples["obs"].reshape([-1, 5]).mean(0),
true_probs,
rtol=0.1)
def test_reparam_log_joint(model, kwargs):
guide = AutoIAFNormal(model)
guide(**kwargs)
neutra = NeuTraReparam(guide)
reparam_model = neutra.reparam(model)
_, pe_fn, transforms, _ = initialize_model(model, model_kwargs=kwargs)
init_params, pe_fn_neutra, _, _ = initialize_model(
reparam_model, model_kwargs=kwargs
)
latent_x = list(init_params.values())[0]
transformed_params = neutra.transform_sample(latent_x)
pe_transformed = pe_fn_neutra(init_params)
neutra_transform = ComposeTransform(guide.get_posterior(**kwargs).transforms)
latent_y = neutra_transform(latent_x)
log_det_jacobian = neutra_transform.log_abs_det_jacobian(latent_x, latent_y)
pe = pe_fn({k: transforms[k](v) for k, v in transformed_params.items()})
assert_close(pe_transformed, pe - log_det_jacobian)
def setup(self, warmup_steps, data):
self.data = data
init_params, potential_fn, transforms, model_trace = initialize_model(self.model,
model_args=(data,))
if self._initial_params is None:
self._initial_params = init_params
if self.transforms is None:
self.transforms = transforms
self._prototype_trace = model_trace
def test_potential_fn_pickling(jit):
data = dist.Bernoulli(torch.tensor(
[0.8, 0.2])).sample(sample_shape=(torch.Size((1000, ))))
_, potential_fn, _, _ = initialize_model(_beta_bernoulli, (data, ),
jit_compile=jit,
skip_jit_warnings=True)
test_data = {'p_latent': torch.tensor([0.2, 0.6])}
assert_close(
pickle.loads(pickle.dumps(potential_fn))(test_data),
potential_fn(test_data))
def test_potential_fn_pickling(jit):
data = dist.Bernoulli(torch.tensor([0.8, 0.2])).sample(sample_shape=(torch.Size((1000,))))
_, potential_fn, _, _ = initialize_model(_beta_bernoulli, (data,), jit_compile=jit,
skip_jit_warnings=True)
test_data = {'p_latent': torch.tensor([0.2, 0.6])}
buffer = io.BytesIO()
torch.save(potential_fn, buffer)
buffer.seek(0)
deser_potential_fn = torch.load(buffer)
assert_close(deser_potential_fn(test_data), potential_fn(test_data))
def run(self, *args, **kwargs):
self._args, self._kwargs = args, kwargs
num_samples = [0] * self.num_chains
z_flat_acc = [[] for _ in range(self.num_chains)]
with pyro.validation_enabled(not self.disable_validation):
for x, chain_id in self.sampler.run(*args, **kwargs):
if num_samples[chain_id] == 0:
num_samples[chain_id] += 1
z_structure = x
elif num_samples[chain_id] == self.num_samples + 1:
self._diagnostics[chain_id] = x
else:
num_samples[chain_id] += 1
if self.num_chains > 1:
x_cloned = x.clone()
del x
else:
x_cloned = x
z_flat_acc[chain_id].append(x_cloned)
z_flat_acc = torch.stack([torch.stack(l) for l in z_flat_acc])
# unpack latent
pos = 0
z_acc = z_structure.copy()
for k in sorted(z_structure):
shape = z_structure[k]
next_pos = pos + shape.numel()
z_acc[k] = z_flat_acc[:, :,
pos:next_pos].reshape((self.num_chains,
self.num_samples) +
shape)
pos = next_pos
assert pos == z_flat_acc.shape[-1]
# If transforms is not explicitly provided, infer automatically using
# model args, kwargs.
if self.transforms is None:
if hasattr(self.kernel, 'transforms'):
if self.kernel.transforms is not None:
self.transforms = self.kernel.transforms
elif self.kernel.model:
_, _, self.transforms, _ = initialize_model(
self.kernel.model, model_args=args, model_kwargs=kwargs)
else:
self.transforms = {}
# transform samples back to constrained space
for name, transform in self.transforms.items():
z_acc[name] = transform.inv(z_acc[name])
self._samples = z_acc
# terminate the sampler (shut down worker processes)
self.sampler.terminate(True)
def setup(self, warmup_steps, *args, **kwargs):
"""Sets up the sampler."""
self._warmup_steps = warmup_steps
init_params, _, _, _ = initialize_model(
self.model,
args,
kwargs,
)
if self._initial_params is None:
self._initial_params = init_params
self._model_args = args
self._model_kwargs = kwargs
def _initialize_model_properties(self, model_args, model_kwargs):
init_params, potential_fn, transforms, trace = initialize_model(
self.model,
model_args,
model_kwargs,
transforms=self.transforms,
max_plate_nesting=self._max_plate_nesting,
jit_compile=self._jit_compile,
jit_options=self._jit_options,
skip_jit_warnings=self._ignore_jit_warnings,
)
self.potential_fn = potential_fn
self.transforms = transforms
if self._initial_params is None:
self.initial_params = init_params
self._prototype_trace = trace
def test_mcmc_diagnostics(num_chains):
data = torch.tensor([2.0]).repeat(3)
initial_params, _, transforms, _ = initialize_model(normal_normal_model,
model_args=(data,),
num_chains=num_chains)
kernel = PriorKernel(normal_normal_model)
mcmc = MCMC(kernel, num_samples=10, warmup_steps=10, num_chains=num_chains, mp_context="spawn",
initial_params=initial_params, transforms=transforms)
mcmc.run(data)
if not torch.backends.mkl.is_available():
pytest.skip()
diagnostics = mcmc.diagnostics()
assert diagnostics["y"]["n_eff"].shape == data.shape
assert diagnostics["y"]["r_hat"].shape == data.shape
assert diagnostics["dummy_key"] == {'chain {}'.format(i): 'dummy_value'
for i in range(num_chains)}
def test_mcmc_diagnostics(run_mcmc_cls, num_chains):
data = torch.tensor([2.0]).repeat(3)
initial_params, _, transforms, _ = initialize_model(normal_normal_model,
model_args=(data, ),
num_chains=num_chains)
kernel = PriorKernel(normal_normal_model)
if run_mcmc_cls == run_default_mcmc:
mcmc = MCMC(
kernel,
num_samples=10,
warmup_steps=10,
num_chains=num_chains,
mp_context="spawn",
initial_params=initial_params,
transforms=transforms,
)
else:
mcmc = StreamingMCMC(
kernel,
num_samples=10,
warmup_steps=10,
num_chains=num_chains,
initial_params=initial_params,
transforms=transforms,
)
mcmc.run(data)
if not torch.backends.mkl.is_available():
pytest.skip()
diagnostics = mcmc.diagnostics()
if run_mcmc_cls == run_default_mcmc: # TODO n_eff for streaming MCMC
assert diagnostics["y"]["n_eff"].shape == data.shape
assert diagnostics["y"]["r_hat"].shape == data.shape
assert diagnostics["dummy_key"] == {
"chain {}".format(i): "dummy_value"
for i in range(num_chains)
}
def infer_sample(
cond_model,
n_steps,
warmup_steps,
n_chains=1,
device="cpu",
guidefile=None,
guide_conf=None,
mcmcfile=None,
):
"""Runs the NUTS HMC algorithm.
Saves the samples and weights as well as a netcdf file for the run.
Parameters
----------
args : dict
Command line arguments.
cond_model : callable
Model conditioned on an observed images.
"""
initial_params, potential_fn, transforms, prototype_trace = util.initialize_model(
cond_model)
if guidefile is not None:
guide = init_guide(cond_model,
guide_conf,
guidefile=guidefile,
device=device)
sample = guide()
for key in initial_params.keys():
initial_params[key] = transforms[key](sample[key].detach())
# FIXME: In the case of DiagonalNormal, results have to be mapped back onto unpacked latents
if guide_conf["type"] == "DiagonalNormal":
transform = guide.get_transform()
unpack_fn = lambda u: guide.unpack_latent(u)
potential_fn = make_transformed_pe(potential_fn, transform, unpack_fn)
initial_params = {"z": torch.zeros(guide.get_posterior().shape())}
transforms = None
def fun(*args, **kwargs):
res = potential_fn(*args, **kwargs)
return res
nuts_kernel = NUTS(
potential_fn=fun,
adapt_step_size=True,
adapt_mass_matrix=True,
full_mass=False,
use_multinomial_sampling=True,
jit_compile=False,
max_tree_depth=10,
transforms=transforms,
step_size=1.0,
)
nuts_kernel.initial_params = initial_params
# Run
mcmc = MCMC(
nuts_kernel,
n_steps,
warmup_steps=warmup_steps,
initial_params=initial_params,
num_chains=n_chains,
)
mcmc.run()
# This block lets the posterior be pickled
mcmc.sampler = None
mcmc.kernel.potential_fn = None
mcmc._cache = {}
print(f"Saving MCMC object to {mcmcfile}")
with open(mcmcfile, "wb") as f:
pickle.dump(mcmc, f, pickle.HIGHEST_PROTOCOL)
def main(args):
baseball_dataset = pd.read_csv(DATA_URL, "\t")
train, _, player_names = train_test_split(baseball_dataset)
at_bats, hits = train[:, 0], train[:, 1]
logging.info("Original Dataset:")
logging.info(baseball_dataset)
# (1) Full Pooling Model
init_params, potential_fn, transforms, _ = initialize_model(fully_pooled, model_args=(at_bats, hits),
num_chains=args.num_chains)
nuts_kernel = NUTS(potential_fn=potential_fn)
mcmc = MCMC(nuts_kernel,
num_samples=args.num_samples,
warmup_steps=args.warmup_steps,
num_chains=args.num_chains,
initial_params=init_params,
transforms=transforms)
mcmc.run(at_bats, hits)
diagnostics = mcmc.diagnostics()
samples_fully_pooled = mcmc.get_samples()
logging.info("\nModel: Fully Pooled")
logging.info("===================")
logging.info("\nphi:")
logging.info(summary(samples_fully_pooled,
sites=["phi"],
player_names=player_names,
diagnostics=diagnostics)["phi"])
num_divergences = sum(map(len, diagnostics["divergences"].values()))
logging.info("\nNumber of divergent transitions: {}\n".format(num_divergences))
sample_posterior_predictive(fully_pooled, samples_fully_pooled, baseball_dataset)
evaluate_log_posterior_density(fully_pooled, samples_fully_pooled, baseball_dataset)
# (2) No Pooling Model
init_params, potential_fn, transforms, _ = initialize_model(not_pooled, model_args=(at_bats, hits),
num_chains=args.num_chains)
nuts_kernel = NUTS(potential_fn=potential_fn)
mcmc = MCMC(nuts_kernel,
num_samples=args.num_samples,
warmup_steps=args.warmup_steps,
num_chains=args.num_chains,
initial_params=init_params,
transforms=transforms)
mcmc.run(at_bats, hits)
diagnostics = mcmc.diagnostics()
samples_not_pooled = mcmc.get_samples()
logging.info("\nModel: Not Pooled")
logging.info("=================")
logging.info("\nphi:")
logging.info(summary(samples_not_pooled,
sites=["phi"],
player_names=player_names,
diagnostics=diagnostics)["phi"])
num_divergences = sum(map(len, diagnostics["divergences"].values()))
logging.info("\nNumber of divergent transitions: {}\n".format(num_divergences))
sample_posterior_predictive(not_pooled, samples_not_pooled, baseball_dataset)
evaluate_log_posterior_density(not_pooled, samples_not_pooled, baseball_dataset)
# (3) Partially Pooled Model
init_params, potential_fn, transforms, _ = initialize_model(partially_pooled, model_args=(at_bats, hits),
num_chains=args.num_chains)
nuts_kernel = NUTS(potential_fn=potential_fn)
mcmc = MCMC(nuts_kernel,
num_samples=args.num_samples,
warmup_steps=args.warmup_steps,
num_chains=args.num_chains,
initial_params=init_params,
transforms=transforms)
mcmc.run(at_bats, hits)
diagnostics = mcmc.diagnostics()
samples_partially_pooled = mcmc.get_samples()
logging.info("\nModel: Partially Pooled")
logging.info("=======================")
logging.info("\nphi:")
logging.info(summary(samples_partially_pooled,
sites=["phi"],
player_names=player_names,
diagnostics=diagnostics)["phi"])
num_divergences = sum(map(len, diagnostics["divergences"].values()))
logging.info("\nNumber of divergent transitions: {}\n".format(num_divergences))
sample_posterior_predictive(partially_pooled, samples_partially_pooled, baseball_dataset)
evaluate_log_posterior_density(partially_pooled, samples_partially_pooled, baseball_dataset)
# (4) Partially Pooled with Logit Model
init_params, potential_fn, transforms, _ = initialize_model(partially_pooled_with_logit,
model_args=(at_bats, hits),
num_chains=args.num_chains)
nuts_kernel = NUTS(potential_fn=potential_fn, transforms=transforms)
mcmc = MCMC(nuts_kernel,
num_samples=args.num_samples,
warmup_steps=args.warmup_steps,
num_chains=args.num_chains,
initial_params=init_params,
transforms=transforms)
mcmc.run(at_bats, hits)
diagnostics = mcmc.diagnostics()
samples_partially_pooled_logit = mcmc.get_samples()
logging.info("\nModel: Partially Pooled with Logit")
logging.info("==================================")
logging.info("\nSigmoid(alpha):")
logging.info(summary(samples_partially_pooled_logit,
sites=["alpha"],
player_names=player_names,
transforms={"alpha": torch.sigmoid},
diagnostics=diagnostics)["alpha"])
num_divergences = sum(map(len, diagnostics["divergences"].values()))
logging.info("\nNumber of divergent transitions: {}\n".format(num_divergences))
sample_posterior_predictive(partially_pooled_with_logit, samples_partially_pooled_logit,
baseball_dataset)
evaluate_log_posterior_density(partially_pooled_with_logit, samples_partially_pooled_logit,
baseball_dataset)
irt_model = irt_model_2pl
elif args.irt_model == '3pl':
if args.hierarchical:
irt_model = irt_model_3pl_hierarchical
else:
irt_model = irt_model_3pl
else:
raise Exception('irt_model {} not supported.'.format(args.irt_model))
init_params, potential_fn, transforms, _ = initialize_model(
irt_model,
model_args=(
args.ability_dim,
num_person,
num_item,
device,
response,
mask,
1,
),
num_chains=args.num_chains,
)
start_time = time.time()
nuts_kernel = NUTS(potential_fn = potential_fn)
mcmc = MCMC(
nuts_kernel,
num_samples = args.num_samples,
warmup_steps = args.num_warmup,
num_chains = args.num_chains,
def main(args):
baseball_dataset = pd.read_csv(DATA_URL, "\t")
train, _, player_names = train_test_split(baseball_dataset)
at_bats, hits = train[:, 0], train[:, 1]
logging.info("Original Dataset:")
logging.info(baseball_dataset)
# (1) Full Pooling Model
# In this model, we illustrate how to use MCMC with general potential_fn.
init_params, potential_fn, transforms, _ = initialize_model(
fully_pooled, model_args=(at_bats, hits), num_chains=args.num_chains,
jit_compile=args.jit, skip_jit_warnings=True)
nuts_kernel = NUTS(potential_fn=potential_fn)
mcmc = MCMC(nuts_kernel,
num_samples=args.num_samples,
warmup_steps=args.warmup_steps,
num_chains=args.num_chains,
initial_params=init_params,
transforms=transforms)
mcmc.run(at_bats, hits)
samples_fully_pooled = mcmc.get_samples()
logging.info("\nModel: Fully Pooled")
logging.info("===================")
logging.info("\nphi:")
logging.info(get_summary_table(mcmc.get_samples(group_by_chain=True),
sites=["phi"],
player_names=player_names,
diagnostics=True,
group_by_chain=True)["phi"])
num_divergences = sum(map(len, mcmc.diagnostics()["divergences"].values()))
logging.info("\nNumber of divergent transitions: {}\n".format(num_divergences))
sample_posterior_predictive(fully_pooled, samples_fully_pooled, baseball_dataset)
evaluate_pointwise_pred_density(fully_pooled, samples_fully_pooled, baseball_dataset)
# (2) No Pooling Model
nuts_kernel = NUTS(not_pooled, jit_compile=args.jit, ignore_jit_warnings=True)
mcmc = MCMC(nuts_kernel,
num_samples=args.num_samples,
warmup_steps=args.warmup_steps,
num_chains=args.num_chains)
mcmc.run(at_bats, hits)
samples_not_pooled = mcmc.get_samples()
logging.info("\nModel: Not Pooled")
logging.info("=================")
logging.info("\nphi:")
logging.info(get_summary_table(mcmc.get_samples(group_by_chain=True),
sites=["phi"],
player_names=player_names,
diagnostics=True,
group_by_chain=True)["phi"])
num_divergences = sum(map(len, mcmc.diagnostics()["divergences"].values()))
logging.info("\nNumber of divergent transitions: {}\n".format(num_divergences))
sample_posterior_predictive(not_pooled, samples_not_pooled, baseball_dataset)
evaluate_pointwise_pred_density(not_pooled, samples_not_pooled, baseball_dataset)
# (3) Partially Pooled Model
nuts_kernel = NUTS(partially_pooled, jit_compile=args.jit, ignore_jit_warnings=True)
mcmc = MCMC(nuts_kernel,
num_samples=args.num_samples,
warmup_steps=args.warmup_steps,
num_chains=args.num_chains)
mcmc.run(at_bats, hits)
samples_partially_pooled = mcmc.get_samples()
logging.info("\nModel: Partially Pooled")
logging.info("=======================")
logging.info("\nphi:")
logging.info(get_summary_table(mcmc.get_samples(group_by_chain=True),
sites=["phi"],
player_names=player_names,
diagnostics=True,
group_by_chain=True)["phi"])
num_divergences = sum(map(len, mcmc.diagnostics()["divergences"].values()))
logging.info("\nNumber of divergent transitions: {}\n".format(num_divergences))
sample_posterior_predictive(partially_pooled, samples_partially_pooled, baseball_dataset)
evaluate_pointwise_pred_density(partially_pooled, samples_partially_pooled, baseball_dataset)
# (4) Partially Pooled with Logit Model
nuts_kernel = NUTS(partially_pooled_with_logit, jit_compile=args.jit, ignore_jit_warnings=True)
mcmc = MCMC(nuts_kernel,
num_samples=args.num_samples,
warmup_steps=args.warmup_steps,
num_chains=args.num_chains)
mcmc.run(at_bats, hits)
samples_partially_pooled_logit = mcmc.get_samples()
logging.info("\nModel: Partially Pooled with Logit")
logging.info("==================================")
logging.info("\nSigmoid(alpha):")
logging.info(get_summary_table(mcmc.get_samples(group_by_chain=True),
sites=["alpha"],
player_names=player_names,
transforms={"alpha": torch.sigmoid},
diagnostics=True,
group_by_chain=True)["alpha"])
num_divergences = sum(map(len, mcmc.diagnostics()["divergences"].values()))
logging.info("\nNumber of divergent transitions: {}\n".format(num_divergences))
sample_posterior_predictive(partially_pooled_with_logit, samples_partially_pooled_logit,
baseball_dataset)
evaluate_pointwise_pred_density(partially_pooled_with_logit, samples_partially_pooled_logit,
baseball_dataset)
def run(self, *args, **kwargs):
"""
Run MCMC to generate samples and populate `self._samples`.
Example usage:
.. code-block:: python
def model(data):
...
nuts_kernel = NUTS(model)
mcmc = MCMC(nuts_kernel, num_samples=500)
mcmc.run(data)
samples = mcmc.get_samples()
:param args: optional arguments taken by
:meth:`MCMCKernel.setup <pyro.infer.mcmc.mcmc_kernel.MCMCKernel.setup>`.
:param kwargs: optional keywords arguments taken by
:meth:`MCMCKernel.setup <pyro.infer.mcmc.mcmc_kernel.MCMCKernel.setup>`.
"""
self._args, self._kwargs = args, kwargs
num_samples = [0] * self.num_chains
z_flat_acc = [[] for _ in range(self.num_chains)]
with optional(pyro.validation_enabled(not self.disable_validation),
self.disable_validation is not None):
for x, chain_id in self.sampler.run(*args, **kwargs):
if num_samples[chain_id] == 0:
num_samples[chain_id] += 1
z_structure = x
elif num_samples[chain_id] == self.num_samples + 1:
self._diagnostics[chain_id] = x
else:
num_samples[chain_id] += 1
if self.num_chains > 1:
x_cloned = x.clone()
del x
else:
x_cloned = x
z_flat_acc[chain_id].append(x_cloned)
z_flat_acc = torch.stack([torch.stack(l) for l in z_flat_acc])
# unpack latent
pos = 0
z_acc = z_structure.copy()
for k in sorted(z_structure):
shape = z_structure[k]
next_pos = pos + shape.numel()
z_acc[k] = z_flat_acc[:, :,
pos:next_pos].reshape((self.num_chains,
self.num_samples) +
shape)
pos = next_pos
assert pos == z_flat_acc.shape[-1]
# If transforms is not explicitly provided, infer automatically using
# model args, kwargs.
if self.transforms is None:
# Try to initialize kernel.transforms using kernel.setup().
if getattr(self.kernel, "transforms", None) is None:
warmup_steps = 0
self.kernel.setup(warmup_steps, *args, **kwargs)
# Use `kernel.transforms` when available
if getattr(self.kernel, "transforms", None) is not None:
self.transforms = self.kernel.transforms
# Else, get transforms from model (e.g. in multiprocessing).
elif self.kernel.model:
_, _, self.transforms, _ = initialize_model(
self.kernel.model,
model_args=args,
model_kwargs=kwargs,
initial_params={})
# Assign default value
else:
self.transforms = {}
# transform samples back to constrained space
for name, transform in self.transforms.items():
z_acc[name] = transform.inv(z_acc[name])
self._samples = z_acc
# terminate the sampler (shut down worker processes)
self.sampler.terminate(True)
