def main() -> None:
x = _load_dataset()
rng_key = random.PRNGKey(0)
rng_key, rng_key_prior, rng_key_infer, rng_key_posterior = random.split(
rng_key, 4)
# prior
predictive = infer.Predictive(model, num_samples=10)
prior_samples = predictive(rng_key_prior, None, *x.shape, future_steps=20)
# Inference
kernel = infer.NUTS(model)
mcmc = infer.MCMC(kernel, 100, 100)
mcmc.run(rng_key_infer, x)
posterior_samples = mcmc.get_samples()
# Posterior prediction
predictive = infer.Predictive(model, posterior_samples=posterior_samples)
posterior_predictive = predictive(rng_key_posterior,
None,
*x.shape,
future_steps=20)
_save_results(x, prior_samples, posterior_samples, posterior_predictive,
len(x))
def main() -> None:
x = jnp.concatenate([
np.random.randn(10),
np.random.randn(10) + 2,
np.random.randn(10) - 1,
np.random.randn(10) + 1,
])
x = x[:, None, None]
rng_key = random.PRNGKey(0)
rng_key, rng_key_prior, rng_key_infer, rng_key_posterior = random.split(
rng_key, 4)
# prior
predictive = infer.Predictive(model, num_samples=10)
prior_samples = predictive(rng_key_prior,
future_steps=20,
batch=10,
x_dim=1)
# Inference
kernel = infer.NUTS(model)
mcmc = infer.MCMC(kernel, 100, 100)
mcmc.run(rng_key_infer, x)
posterior_samples = mcmc.get_samples()
# Posterior prediction
predictive = infer.Predictive(model, posterior_samples=posterior_samples)
posterior_predictive = predictive(rng_key_posterior, None, *x.shape, 10)
_save_results(x, prior_samples, posterior_samples, posterior_predictive)
def main() -> None:
# Data
x, t = _load_data()
num_train = int(len(x) * 0.8)
x_train = x[:num_train]
t_train = t[:num_train]
rng_key = random.PRNGKey(0)
rng_key, rng_key_prior, rng_key_infer, rng_key_posterior = random.split(
rng_key, 4)
# prior
predictive = infer.Predictive(model, num_samples=10)
prior_samples = predictive(rng_key_prior, t)
# Inference
kernel = infer.NUTS(model)
mcmc = infer.MCMC(kernel, 100, 100)
mcmc.run(rng_key_infer, t_train, x_train)
posterior_samples = mcmc.get_samples()
# Posterior prediction
predictive = infer.Predictive(
model,
posterior_samples=posterior_samples,
return_sites=["x", "s0", "z", "trend", "weight"],
)
posterior_predictive = predictive(rng_key_posterior, t)
_save_results(x, prior_samples, posterior_samples, posterior_predictive,
num_train)
def main() -> None:
# Data
x, betas, covariates = _load_data()
num_train = int(len(x) * 0.8)
x_train = x[:num_train]
c_train = covariates[:num_train]
rng_key = random.PRNGKey(0)
rng_key, rng_key_prior, rng_key_infer, rng_key_posterior = random.split(
rng_key, 4)
# prior
predictive = infer.Predictive(model, num_samples=10)
prior_samples = predictive(rng_key_prior, c_train)
# Inference
kernel = infer.NUTS(model)
mcmc = infer.MCMC(kernel, 100, 100)
mcmc.run(rng_key_infer, c_train, x_train)
posterior_samples = mcmc.get_samples()
# Posterior prediction
posterior_given = posterior_samples.copy()
posterior_given.pop("weight")
predictive = infer.Predictive(model,
posterior_samples=posterior_given,
return_sites=["x", "weight"])
posterior_predictive = predictive(rng_key_posterior, covariates)
_save_results(x, betas, prior_samples, posterior_samples,
posterior_predictive, num_train)
def main() -> None:
_, y, x_missing = _load_dataset()
train_len = int(len(y) * 0.8)
x_train = x_missing[:train_len]
y_train = y[:train_len]
num_chains = 1
numpyro.set_platform("cpu")
numpyro.set_host_device_count(num_chains)
rng_key = random.PRNGKey(0)
rng_key, rng_key_posterior, rng_key_prior = random.split(rng_key, 3)
predictive = infer.Predictive(bayesian_regression, num_samples=500)
prior = predictive(rng_key_prior, x_train)
kernel = infer.NUTS(bayesian_regression)
mcmc = infer.MCMC(kernel,
num_warmup=1000,
num_samples=1000,
num_chains=num_chains)
mcmc.run(rng_key, x_train, y_train)
posterior_samples = mcmc.get_samples()
predictive = infer.Predictive(bayesian_regression,
posterior_samples=posterior_samples)
posterior_predictive = predictive(rng_key_posterior, x_missing)
_save_results(y, mcmc, prior, posterior_samples, posterior_predictive)
def sample_posterior_predictive(
self,
df: pd.DataFrame,
hdpi: bool = False,
hdpi_interval: float = 0.9,
rng_key: np.ndarray = None,
) -> typing.Union[pd.Series, pd.DataFrame]:
"""Obtain samples from the posterior predictive.
Parameters
----------
df : pd.DataFrame
Source dataframe.
hdpi : bool
Option to include lower/upper bound of the highest posterior density
interval. Returns a dataframe if true, a series if false. Default False.
hdpi_interval : float
HDPI width. Default 0.9.
rng_key : two-element ndarray.
Optional rng key, will be randomly splitted if not provided.
Returns
-------
pd.Series or pd.DataFrame
Forecasts. Will be a series with the name of the dv if no HDPI. Will be a
dataframe if HDPI is included.
"""
# get rng key
rng_key_ = (self.split_rand_key()
if rng_key is None else rng_key.astype("uint32"))
# check for nulls
null_cols = columns_with_null_data(self.transform(df))
if null_cols:
raise exceptions.NullDataFound(*null_cols)
# do it
predictions = infer.Predictive(self.model,
self.samples_flat)(rng_key_,
df=df)[self.dv]
if not hdpi:
return pd.Series(predictions.mean(axis=0),
index=df.index,
name=self.dv)
hdpi = diagnostics.hpdi(predictions, hdpi_interval)
return pd.DataFrame(
{
self.dv: predictions.mean(axis=0),
"hdpi_lower": hdpi[0, :],
"hdpi_upper": hdpi[1, :],
},
index=df.index,
)
def main(args: argparse.Namespace) -> None:
_, y, x_missing = _load_dataset()
batch, x_dim = x_missing.shape
train_len = int(len(y) * 0.8)
x_train = x_missing[:train_len]
y_train = y[:train_len]
numpyro.set_platform("cpu")
numpyro.set_host_device_count(args.num_chains)
rng_key = random.PRNGKey(1)
rng_key, rng_key_prior, rng_key_posterior, rng_key_pca_pred = random.split(
rng_key, 4)
predictive = infer.Predictive(pca_regression, num_samples=500)
prior = predictive(rng_key_prior, batch=batch, x_dim=x_dim)
kernel = infer.NUTS(pca_regression)
mcmc = infer.MCMC(
kernel,
num_warmup=args.num_warmup,
num_samples=args.num_samples,
num_chains=args.num_chains,
)
mcmc.run(rng_key_posterior, x_train, y_train)
posterior_samples = mcmc.get_samples()
posterior_without_z = posterior_samples.copy()
posterior_without_z.pop("z")
predictive = infer.Predictive(pca_regression,
posterior_samples=posterior_without_z)
posterior_predictive = predictive(rng_key_pca_pred,
batch=batch,
x_dim=x_dim)
_save_results(
y,
mcmc,
prior,
posterior_samples,
posterior_predictive,
var_names=["phi", "eta", "theta", "sigma"],
)
def main() -> None:
x = _load_data()
rng_key = random.PRNGKey(0)
rng_key, rng_key_prior, rng_key_infer, rng_key_posterior = random.split(rng_key, 4)
# Prior prediction
predictive = infer.Predictive(model, num_samples=10)
prior_predictive = predictive(rng_key_prior, None, *x.shape, future_steps=10)
# Inference
adam = optim.Adam(0.0001)
svi = infer.SVI(model, guide, adam, infer.Trace_ELBO())
svi_result = svi.run(rng_key_infer, 50000, x)
# Posterior prediction
predictive = infer.Predictive(model, params=svi_result.params, num_samples=10)
posterior_predictive = predictive(rng_key_posterior, None, *x.shape, future_steps=10)
_save_results(x, svi_result.params, prior_predictive, posterior_predictive, svi_result)
def main(args: argparse.Namespace) -> None:
model = model_dict[args.model]
_, fetch = load_dataset(JSB_CHORALES, split="train", shuffle=False)
lengths, sequences = fetch()
# Remove never used data dimension to reduce computation time
present_notes = (sequences == 1).sum(0).sum(0) > 0
sequences = sequences[..., present_notes]
batch, seq_len, data_dim = sequences.shape
rng_key = random.PRNGKey(0)
rng_key, rng_key_prior, rng_key_pred = random.split(rng_key, 3)
predictive = infer.Predictive(model, num_samples=10)
prior_samples = predictive(rng_key_prior,
batch=batch,
seq_len=seq_len,
data_dim=data_dim,
future_steps=20)
kernel = infer.NUTS(model)
mcmc = infer.MCMC(kernel, args.num_warmup, args.num_samples,
args.num_chains)
mcmc.run(rng_key, sequences, lengths)
posterior_samples = mcmc.get_samples()
predictive = infer.Predictive(model, posterior_samples)
predictive_samples = predictive(rng_key_pred,
sequences,
lengths,
future_steps=10)
path = pathlib.Path("./data/hmm_enum")
path.mkdir(exist_ok=True)
jnp.savez(path / "prior_samples.npz", **prior_samples)
jnp.savez(path / "posterior_samples.npz", **posterior_samples)
jnp.savez(path / "predictive_samples.npz", **predictive_samples)
def main() -> None:
rng_key = random.PRNGKey(0)
samples = run_inference(model, rng_key)
reparam_samples = run_inference(reparam_model, rng_key)
predictive = infer.Predictive(reparam_model,
reparam_samples,
return_sites=["x", "y"])
reparam_samples = predictive(random.PRNGKey(1))
_plot_results(samples, reparam_samples)
def predict(
model: Callable,
sigma: np.ndarray,
rng_key: np.ndarray,
*,
posterior_samples: Optional[Dict[str, jnp.ndarray]] = None,
num_samples: Optional[int] = None,
) -> Dict[str, jnp.ndarray]:
predictive = infer.Predictive(model,
posterior_samples=posterior_samples,
num_samples=num_samples)
return predictive(rng_key, sigma)