Created
January 4, 2022 05:01
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| from collections import namedtuple | |
| from jax import random | |
| from jax.flatten_util import ravel_pytree | |
| import jax.numpy as jnp | |
| import numpyro | |
| import numpyro.distributions as dist | |
| from numpyro.infer import MCMC, util | |
| from numpyro.infer.mcmc import MCMCKernel | |
| MetState = namedtuple("MetState", ["z", "rng_key"]) # does it matter if it is called z or u? | |
| class Metropolis(MCMCKernel): | |
| def __init__(self, model, step_size=0.1): | |
| self._model = model | |
| self._step_size = step_size | |
| @property | |
| def sample_field(self): | |
| return "z" | |
| @property | |
| def default_fields(self): | |
| return ("z",) | |
| def init(self, rng_key, num_warmup, init_params, model_args, model_kwargs): | |
| assert rng_key.ndim == 1, "only non-vectorized, for now" | |
| return MetState(init_params, rng_key) | |
| def sample(self, state, model_args, model_kwargs): | |
| rng_key, key_proposal, key_accept = random.split(state.rng_key, 3) | |
| z_flat, unravel_fn = ravel_pytree(state.z) | |
| z_proposal = dist.Normal(z_flat, self._step_size).sample(key_proposal) | |
| z_proposal_dict = unravel_fn(z_proposal) | |
| log_pr_0, model_tr = util.log_density(self._model, model_args, model_kwargs, state.z) | |
| log_pr_1, model_tr = util.log_density(self._model, model_args, model_kwargs, z_proposal_dict) | |
| accept_prob = jnp.exp(log_pr_1 - log_pr_0) | |
| z_new = jnp.where(dist.Uniform().sample(key_accept) < accept_prob, z_proposal, z_flat) | |
| return MetState(unravel_fn(z_new), rng_key) | |
| def model(): | |
| numpyro.sample('x', dist.Normal(0,1)) | |
| rng_key = random.PRNGKey(12345) | |
| kernel = Metropolis(model, step_size=1) | |
| mcmc = MCMC(kernel, num_warmup=0, num_samples=200, thinning=1) | |
| mcmc.run(rng_key, init_params={'x':0}) | |
| posterior_samples = mcmc.get_samples() | |
| mcmc.print_summary() |
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