def test_logistic_regression(jit, num_chains):
dim = 3
data = torch.randn(2000, dim)
true_coefs = torch.arange(1.0, dim + 1.0)
labels = dist.Bernoulli(logits=(true_coefs * data).sum(-1)).sample()
def model(data):
coefs_mean = torch.zeros(dim)
coefs = pyro.sample("beta", dist.Normal(coefs_mean, torch.ones(dim)))
y = pyro.sample("y",
dist.Bernoulli(logits=(coefs * data).sum(-1)),
obs=labels)
return y
slice_kernel = Slice(model, jit_compile=jit, ignore_jit_warnings=True)
mcmc = MCMC(
slice_kernel,
num_samples=500,
warmup_steps=100,
num_chains=num_chains,
mp_context="fork",
available_cpu=1,
)
mcmc.run(data)
samples = mcmc.get_samples()
assert_equal(rmse(true_coefs, samples["beta"].mean(0)).item(),
0.0,
prec=0.1)
def test_beta_binomial(hyperpriors):
def model(data):
with pyro.plate("plate_0", data.shape[-1]):
alpha = (pyro.sample("alpha", dist.HalfCauchy(1.0))
if hyperpriors else torch.tensor([1.0, 1.0]))
beta = (pyro.sample("beta", dist.HalfCauchy(1.0))
if hyperpriors else torch.tensor([1.0, 1.0]))
beta_binom = BetaBinomialPair()
with pyro.plate("plate_1", data.shape[-2]):
probs = pyro.sample("probs", beta_binom.latent(alpha, beta))
with pyro.plate("data", data.shape[0]):
pyro.sample(
"binomial",
beta_binom.conditional(probs=probs,
total_count=total_count),
obs=data,
)
true_probs = torch.tensor([[0.7, 0.4], [0.6, 0.4]])
total_count = torch.tensor([[1000, 600], [400, 800]])
num_samples = 80
data = dist.Binomial(
total_count=total_count,
probs=true_probs).sample(sample_shape=(torch.Size((10, ))))
hmc_kernel = Slice(collapse_conjugate(model),
jit_compile=True,
ignore_jit_warnings=True)
mcmc = MCMC(hmc_kernel, num_samples=num_samples, warmup_steps=50)
mcmc.run(data)
samples = mcmc.get_samples()
posterior = posterior_replay(model, samples, data, num_samples=num_samples)
assert_equal(posterior["probs"].mean(0), true_probs, prec=0.05)
def test_gaussian_mixture_model(jit):
K, N = 3, 1000
def gmm(data):
mix_proportions = pyro.sample("phi", dist.Dirichlet(torch.ones(K)))
with pyro.plate("num_clusters", K):
cluster_means = pyro.sample(
"cluster_means", dist.Normal(torch.arange(float(K)), 1.0))
with pyro.plate("data", data.shape[0]):
assignments = pyro.sample("assignments",
dist.Categorical(mix_proportions))
pyro.sample("obs",
dist.Normal(cluster_means[assignments], 1.0),
obs=data)
return cluster_means
true_cluster_means = torch.tensor([1.0, 5.0, 10.0])
true_mix_proportions = torch.tensor([0.1, 0.3, 0.6])
cluster_assignments = dist.Categorical(true_mix_proportions).sample(
torch.Size((N, )))
data = dist.Normal(true_cluster_means[cluster_assignments], 1.0).sample()
slice_kernel = Slice(gmm,
max_plate_nesting=1,
jit_compile=jit,
ignore_jit_warnings=True)
mcmc = MCMC(slice_kernel, num_samples=300, warmup_steps=100)
mcmc.run(data)
samples = mcmc.get_samples()
assert_equal(samples["phi"].mean(0).sort()[0],
true_mix_proportions,
prec=0.05)
assert_equal(samples["cluster_means"].mean(0).sort()[0],
true_cluster_means,
prec=0.2)
def test_gamma_normal(jit):
def model(data):
rate = torch.tensor([1.0, 1.0])
concentration = torch.tensor([1.0, 1.0])
p_latent = pyro.sample("p_latent", dist.Gamma(rate, concentration))
pyro.sample("obs", dist.Normal(3, p_latent), obs=data)
return p_latent
true_std = torch.tensor([0.5, 2])
data = dist.Normal(3, true_std).sample(sample_shape=(torch.Size((2000, ))))
slice_kernel = Slice(model, jit_compile=jit, ignore_jit_warnings=True)
mcmc = MCMC(slice_kernel, num_samples=200, warmup_steps=100)
mcmc.run(data)
samples = mcmc.get_samples()
assert_equal(samples["p_latent"].mean(0), true_std, prec=0.05)
def test_dirichlet_categorical(jit):
def model(data):
concentration = torch.tensor([1.0, 1.0, 1.0])
p_latent = pyro.sample("p_latent", dist.Dirichlet(concentration))
pyro.sample("obs", dist.Categorical(p_latent), obs=data)
return p_latent
true_probs = torch.tensor([0.1, 0.6, 0.3])
data = dist.Categorical(true_probs).sample(
sample_shape=(torch.Size((2000, ))))
slice_kernel = Slice(model, jit_compile=jit, ignore_jit_warnings=True)
mcmc = MCMC(slice_kernel, num_samples=200, warmup_steps=100)
mcmc.run(data)
samples = mcmc.get_samples()
posterior = samples["p_latent"]
assert_equal(posterior.mean(0), true_probs, prec=0.02)
def test_beta_bernoulli():
def model(data):
alpha = torch.tensor([1.1, 1.1])
beta = torch.tensor([1.1, 1.1])
p_latent = pyro.sample("p_latent", dist.Beta(alpha, beta))
pyro.sample("obs", dist.Bernoulli(p_latent), obs=data)
return p_latent
true_probs = torch.tensor([0.9, 0.1])
data = dist.Bernoulli(true_probs).sample(
sample_shape=(torch.Size((1000, ))))
slice_kernel = Slice(model)
mcmc = MCMC(slice_kernel, num_samples=400, warmup_steps=200)
mcmc.run(data)
samples = mcmc.get_samples()
assert_equal(samples["p_latent"].mean(0), true_probs, prec=0.02)
def test_bernoulli_latent_model(jit):
@poutine.broadcast
def model(data):
y_prob = pyro.sample("y_prob", dist.Beta(1.0, 1.0))
with pyro.plate("data", data.shape[0]):
y = pyro.sample("y", dist.Bernoulli(y_prob))
z = pyro.sample("z", dist.Bernoulli(0.65 * y + 0.1))
pyro.sample("obs", dist.Normal(2.0 * z, 1.0), obs=data)
N = 2000
y_prob = torch.tensor(0.3)
y = dist.Bernoulli(y_prob).sample(torch.Size((N, )))
z = dist.Bernoulli(0.65 * y + 0.1).sample()
data = dist.Normal(2.0 * z, 1.0).sample()
slice_kernel = Slice(model,
max_plate_nesting=1,
jit_compile=jit,
ignore_jit_warnings=True)
mcmc = MCMC(slice_kernel, num_samples=600, warmup_steps=200)
mcmc.run(data)
samples = mcmc.get_samples()
assert_equal(samples["y_prob"].mean(0), y_prob, prec=0.05)
def test_gamma_beta(jit):
def model(data):
alpha_prior = pyro.sample("alpha",
dist.Gamma(concentration=1.0, rate=1.0))
beta_prior = pyro.sample("beta", dist.Gamma(concentration=1.0,
rate=1.0))
pyro.sample(
"x",
dist.Beta(concentration1=alpha_prior, concentration0=beta_prior),
obs=data,
)
true_alpha = torch.tensor(5.0)
true_beta = torch.tensor(1.0)
data = dist.Beta(concentration1=true_alpha,
concentration0=true_beta).sample(torch.Size((5000, )))
slice_kernel = Slice(model, jit_compile=jit, ignore_jit_warnings=True)
mcmc = MCMC(slice_kernel, num_samples=500, warmup_steps=200)
mcmc.run(data)
samples = mcmc.get_samples()
assert_equal(samples["alpha"].mean(0), true_alpha, prec=0.08)
assert_equal(samples["beta"].mean(0), true_beta, prec=0.05)
def test_slice_conjugate_gaussian(
fixture,
num_samples,
warmup_steps,
expected_means,
expected_precs,
mean_tol,
std_tol,
):
pyro.get_param_store().clear()
slice_kernel = Slice(fixture.model)
mcmc = MCMC(slice_kernel, num_samples, warmup_steps, num_chains=3)
mcmc.run(fixture.data)
samples = mcmc.get_samples()
for i in range(1, fixture.chain_len + 1):
param_name = "loc_" + str(i)
latent = samples[param_name]
latent_loc = latent.mean(0)
latent_std = latent.std(0)
expected_mean = torch.ones(fixture.dim) * expected_means[i - 1]
expected_std = 1 / torch.sqrt(
torch.ones(fixture.dim) * expected_precs[i - 1])
# Actual vs expected posterior means for the latents
logger.debug("Posterior mean (actual) - {}".format(param_name))
logger.debug(latent_loc)
logger.debug("Posterior mean (expected) - {}".format(param_name))
logger.debug(expected_mean)
assert_equal(rmse(latent_loc, expected_mean).item(),
0.0,
prec=mean_tol)
# Actual vs expected posterior precisions for the latents
logger.debug("Posterior std (actual) - {}".format(param_name))
logger.debug(latent_std)
logger.debug("Posterior std (expected) - {}".format(param_name))
logger.debug(expected_std)
assert_equal(rmse(latent_std, expected_std).item(), 0.0, prec=std_tol)
def test_gamma_poisson(hyperpriors):
def model(data):
with pyro.plate("latent_dim", data.shape[1]):
alpha = (pyro.sample("alpha", dist.HalfCauchy(1.0))
if hyperpriors else torch.tensor([1.0, 1.0]))
beta = (pyro.sample("beta", dist.HalfCauchy(1.0))
if hyperpriors else torch.tensor([1.0, 1.0]))
gamma_poisson = GammaPoissonPair()
rate = pyro.sample("rate", gamma_poisson.latent(alpha, beta))
with pyro.plate("data", data.shape[0]):
pyro.sample("obs", gamma_poisson.conditional(rate), obs=data)
true_rate = torch.tensor([3.0, 10.0])
num_samples = 100
data = dist.Poisson(rate=true_rate).sample(
sample_shape=(torch.Size((100, ))))
slice_kernel = Slice(collapse_conjugate(model),
jit_compile=True,
ignore_jit_warnings=True)
mcmc = MCMC(slice_kernel, num_samples=num_samples, warmup_steps=50)
mcmc.run(data)
samples = mcmc.get_samples()
posterior = posterior_replay(model, samples, data, num_samples=num_samples)
assert_equal(posterior["rate"].mean(0), true_rate, prec=0.3)
def run(
task: Task,
num_samples: int,
num_observation: Optional[int] = None,
observation: Optional[torch.Tensor] = None,
num_chains: int = 10,
num_warmup: int = 10000,
kernel: str = "slice",
kernel_parameters: Optional[Dict[str, Any]] = None,
thinning: int = 1,
diagnostics: bool = True,
available_cpu: int = 1,
mp_context: str = "fork",
jit_compile: bool = False,
automatic_transforms_enabled: bool = True,
initial_params: Optional[torch.Tensor] = None,
**kwargs: Any,
) -> torch.Tensor:
"""Runs MCMC using Pyro on potential function
Produces `num_samples` while accounting for warmup (burn-in) and thinning.
Note that the actual number of simulations is not controlled for with MCMC since
algorithms are only used as a reference method in the benchmark.
MCMC is run on the potential function, which returns the unnormalized
negative log posterior probability. Note that this requires a tractable likelihood.
Pyro is used to automatically construct the potential function.
Args:
task: Task instance
num_samples: Number of samples to generate from posterior
num_observation: Observation number to load, alternative to `observation`
observation: Observation, alternative to `num_observation`
num_chains: Number of chains
num_warmup: Warmup steps, during which parameters of the sampler are adapted.
Warmup samples are not returned by the algorithm.
kernel: HMC, NUTS, or Slice
kernel_parameters: Parameters passed to kernel
thinning: Amount of thinning to apply, in order to avoid drawing
correlated samples from the chain
diagnostics: Flag for diagnostics
available_cpu: Number of CPUs used to parallelize chains
mp_context: multiprocessing context, only fork might work
jit_compile: Just-in-time (JIT) compilation, can yield significant speed ups
automatic_transforms_enabled: Whether or not to use automatic transforms
initial_params: Parameters to initialize at
Returns:
Samples from posterior
"""
assert not (num_observation is None and observation is None)
assert not (num_observation is not None and observation is not None)
tic = time.time()
log = sbibm.get_logger(__name__)
hook_fn = None
if diagnostics:
log.info(f"MCMC sampling for observation {num_observation}")
tb_writer, tb_close = tb_make_writer(
logger=log,
basepath=
f"tensorboard/pyro_{kernel.lower()}/observation_{num_observation}",
)
hook_fn = tb_make_hook_fn(tb_writer)
if "num_simulations" in kwargs:
warnings.warn(
"`num_simulations` was passed as a keyword but will be ignored, see docstring for more info."
)
# Prepare model and transforms
conditioned_model = task._get_pyro_model(num_observation=num_observation,
observation=observation)
transforms = task._get_transforms(
num_observation=num_observation,
observation=observation,
automatic_transforms_enabled=automatic_transforms_enabled,
)
kernel_parameters = kernel_parameters if kernel_parameters is not None else {}
kernel_parameters["jit_compile"] = jit_compile
kernel_parameters["transforms"] = transforms
log.info("Using kernel: {name}({parameters})".format(
name=kernel,
parameters=",".join([f"{k}={v}"
for k, v in kernel_parameters.items()]),
))
if kernel.lower() == "nuts":
mcmc_kernel = NUTS(model=conditioned_model, **kernel_parameters)
elif kernel.lower() == "hmc":
mcmc_kernel = HMC(model=conditioned_model, **kernel_parameters)
elif kernel.lower() == "slice":
mcmc_kernel = Slice(model=conditioned_model, **kernel_parameters)
else:
raise NotImplementedError
if initial_params is not None:
site_name = "parameters"
initial_params = {site_name: transforms[site_name](initial_params)}
else:
initial_params = None
mcmc_parameters = {
"num_chains": num_chains,
"num_samples": thinning * num_samples,
"warmup_steps": num_warmup,
"available_cpu": available_cpu,
"initial_params": initial_params,
}
log.info("Calling MCMC with: MCMC({name}_kernel, {parameters})".format(
name=kernel,
parameters=",".join([f"{k}={v}" for k, v in mcmc_parameters.items()]),
))
mcmc = MCMC(mcmc_kernel, hook_fn=hook_fn, **mcmc_parameters)
mcmc.run()
toc = time.time()
log.info(f"Finished MCMC after {toc-tic:.3f} seconds")
log.info(f"Automatic transforms {mcmc.transforms}")
log.info(f"Apply thinning of {thinning}")
mcmc._samples = {
"parameters": mcmc._samples["parameters"][:, ::thinning, :]
}
num_samples_available = (mcmc._samples["parameters"].shape[0] *
mcmc._samples["parameters"].shape[1])
if num_samples_available < num_samples:
warnings.warn("Some samples will be included multiple times")
samples = mcmc.get_samples(
num_samples=num_samples,
group_by_chain=False)["parameters"].squeeze()
else:
samples = mcmc.get_samples(
group_by_chain=False)["parameters"].squeeze()
idx = torch.randperm(samples.shape[0])[:num_samples]
samples = samples[idx, :]
assert samples.shape[0] == num_samples
if diagnostics:
mcmc.summary()
tb_ess(tb_writer, mcmc)
tb_r_hat(tb_writer, mcmc)
tb_marginals(tb_writer, mcmc)
tb_acf(tb_writer, mcmc)
tb_posteriors(tb_writer, mcmc)
tb_plot_posterior(tb_writer, samples, tag="posterior/final")
tb_close()
return samples