Source code for numpyro.infer.mcmc

from abc import ABC, abstractmethod
from collections import namedtuple
import functools
import math
from operator import attrgetter
import os
import warnings

from jax import jit, lax, partial, pmap, random, vmap
from jax.flatten_util import ravel_pytree
from jax.lib import xla_bridge
import jax.numpy as np
from jax.random import PRNGKey
from jax.tree_util import tree_flatten, tree_map, tree_multimap

from numpyro.diagnostics import print_summary
from numpyro.infer.hmc_util import (
    IntegratorState,
    build_tree,
    euclidean_kinetic_energy,
    find_reasonable_step_size,
    velocity_verlet,
    warmup_adapter
)
from numpyro.infer.util import init_to_uniform, initialize_model
from numpyro.util import cond, copy_docs_from, fori_collect, fori_loop, identity

HMCState = namedtuple('HMCState', ['i', 'z', 'z_grad', 'potential_energy', 'energy', 'num_steps', 'accept_prob',
                                   'mean_accept_prob', 'diverging', 'adapt_state', 'rng_key'])
"""
A :func:`~collections.namedtuple` consisting of the following fields:

 - **i** - iteration. This is reset to 0 after warmup.
 - **z** - Python collection representing values (unconstrained samples from
   the posterior) at latent sites.
 - **z_grad** - Gradient of potential energy w.r.t. latent sample sites.
 - **potential_energy** - Potential energy computed at the given value of ``z``.
 - **energy** - Sum of potential energy and kinetic energy of the current state.
 - **num_steps** - Number of steps in the Hamiltonian trajectory (for diagnostics).
 - **accept_prob** - Acceptance probability of the proposal. Note that ``z``
   does not correspond to the proposal if it is rejected.
 - **mean_accept_prob** - Mean acceptance probability until current iteration
   during warmup adaptation or sampling (for diagnostics).
 - **diverging** - A boolean value to indicate whether the current trajectory is diverging.
 - **adapt_state** - A ``AdaptState`` namedtuple which contains adaptation information
   during warmup:

   + **step_size** - Step size to be used by the integrator in the next iteration.
   + **inverse_mass_matrix** - The inverse mass matrix to be used for the next
     iteration.
   + **mass_matrix_sqrt** - The square root of mass matrix to be used for the next
     iteration. In case of dense mass, this is the Cholesky factorization of the
     mass matrix.

 - **rng_key** - random number generator seed used for the iteration.
"""


def _get_num_steps(step_size, trajectory_length):
    num_steps = np.clip(trajectory_length / step_size, a_min=1)
    # NB: casting to np.int64 does not take effect (returns np.int32 instead)
    # if jax_enable_x64 is False
    return num_steps.astype(xla_bridge.canonicalize_dtype(np.int64))


def _sample_momentum(unpack_fn, mass_matrix_sqrt, rng_key):
    eps = random.normal(rng_key, np.shape(mass_matrix_sqrt)[:1])
    if mass_matrix_sqrt.ndim == 1:
        r = np.multiply(mass_matrix_sqrt, eps)
        return unpack_fn(r)
    elif mass_matrix_sqrt.ndim == 2:
        r = np.dot(mass_matrix_sqrt, eps)
        return unpack_fn(r)
    else:
        raise ValueError("Mass matrix has incorrect number of dims.")


def get_diagnostics_str(hmc_state):
    return '{} steps of size {:.2e}. acc. prob={:.2f}'.format(hmc_state.num_steps,
                                                              hmc_state.adapt_state.step_size,
                                                              hmc_state.mean_accept_prob)


def get_progbar_desc_str(num_warmup, i):
    if i < num_warmup:
        return 'warmup'
    return 'sample'


[docs]def hmc(potential_fn, kinetic_fn=None, algo='NUTS'): r""" Hamiltonian Monte Carlo inference, using either fixed number of steps or the No U-Turn Sampler (NUTS) with adaptive path length. **References:** 1. *MCMC Using Hamiltonian Dynamics*, Radford M. Neal 2. *The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo*, Matthew D. Hoffman, and Andrew Gelman. 3. *A Conceptual Introduction to Hamiltonian Monte Carlo`*, Michael Betancourt :param potential_fn: Python callable that computes the potential energy given input parameters. The input parameters to `potential_fn` can be any python collection type, provided that `init_params` argument to `init_kernel` has the same type. :param kinetic_fn: Python callable that returns the kinetic energy given inverse mass matrix and momentum. If not provided, the default is euclidean kinetic energy. :param str algo: Whether to run ``HMC`` with fixed number of steps or ``NUTS`` with adaptive path length. Default is ``NUTS``. :return: a tuple of callables (`init_kernel`, `sample_kernel`), the first one to initialize the sampler, and the second one to generate samples given an existing one. .. warning:: Instead of using this interface directly, we would highly recommend you to use the higher level :class:`numpyro.infer.MCMC` API instead. **Example** .. testsetup:: import jax from jax import random import jax.numpy as np import numpyro import numpyro.distributions as dist from numpyro.infer.mcmc import hmc from numpyro.infer.util import initialize_model from numpyro.util import fori_collect .. doctest:: >>> true_coefs = np.array([1., 2., 3.]) >>> data = random.normal(random.PRNGKey(2), (2000, 3)) >>> dim = 3 >>> labels = dist.Bernoulli(logits=(true_coefs * data).sum(-1)).sample(random.PRNGKey(3)) >>> >>> def model(data, labels): ... coefs_mean = np.zeros(dim) ... coefs = numpyro.sample('beta', dist.Normal(coefs_mean, np.ones(3))) ... intercept = numpyro.sample('intercept', dist.Normal(0., 10.)) ... return numpyro.sample('y', dist.Bernoulli(logits=(coefs * data + intercept).sum(-1)), obs=labels) >>> >>> init_params, potential_fn, constrain_fn = initialize_model(random.PRNGKey(0), ... model, data, labels) >>> init_kernel, sample_kernel = hmc(potential_fn, algo='NUTS') >>> hmc_state = init_kernel(init_params, ... trajectory_length=10, ... num_warmup=300) >>> samples = fori_collect(0, 500, sample_kernel, hmc_state, ... transform=lambda state: constrain_fn(state.z)) >>> print(np.mean(samples['beta'], axis=0)) # doctest: +SKIP [0.9153987 2.0754058 2.9621222] """ if kinetic_fn is None: kinetic_fn = euclidean_kinetic_energy vv_init, vv_update = velocity_verlet(potential_fn, kinetic_fn) trajectory_len = None max_treedepth = None momentum_generator = None wa_update = None wa_steps = None max_delta_energy = 1000. if algo not in {'HMC', 'NUTS'}: raise ValueError('`algo` must be one of `HMC` or `NUTS`.') def init_kernel(init_params, num_warmup, step_size=1.0, inverse_mass_matrix=None, adapt_step_size=True, adapt_mass_matrix=True, dense_mass=False, target_accept_prob=0.8, trajectory_length=2*math.pi, max_tree_depth=10, rng_key=PRNGKey(0)): """ Initializes the HMC sampler. :param init_params: Initial parameters to begin sampling. The type must be consistent with the input type to `potential_fn`. :param int num_warmup: Number of warmup steps; samples generated during warmup are discarded. :param float step_size: Determines the size of a single step taken by the verlet integrator while computing the trajectory using Hamiltonian dynamics. If not specified, it will be set to 1. :param numpy.ndarray inverse_mass_matrix: Initial value for inverse mass matrix. This may be adapted during warmup if adapt_mass_matrix = True. If no value is specified, then it is initialized to the identity matrix. :param bool adapt_step_size: A flag to decide if we want to adapt step_size during warm-up phase using Dual Averaging scheme. :param bool adapt_mass_matrix: A flag to decide if we want to adapt mass matrix during warm-up phase using Welford scheme. :param bool dense_mass: A flag to decide if mass matrix is dense or diagonal (default when ``dense_mass=False``) :param float target_accept_prob: Target acceptance probability for step size adaptation using Dual Averaging. Increasing this value will lead to a smaller step size, hence the sampling will be slower but more robust. Default to 0.8. :param float trajectory_length: Length of a MCMC trajectory for HMC. Default value is :math:`2\\pi`. :param int max_tree_depth: Max depth of the binary tree created during the doubling scheme of NUTS sampler. Defaults to 10. :param jax.random.PRNGKey rng_key: random key to be used as the source of randomness. """ step_size = lax.convert_element_type(step_size, xla_bridge.canonicalize_dtype(np.float64)) nonlocal momentum_generator, wa_update, trajectory_len, max_treedepth, wa_steps wa_steps = num_warmup trajectory_len = trajectory_length max_treedepth = max_tree_depth z = init_params z_flat, unravel_fn = ravel_pytree(z) momentum_generator = partial(_sample_momentum, unravel_fn) find_reasonable_ss = partial(find_reasonable_step_size, potential_fn, kinetic_fn, momentum_generator) wa_init, wa_update = warmup_adapter(num_warmup, adapt_step_size=adapt_step_size, adapt_mass_matrix=adapt_mass_matrix, dense_mass=dense_mass, target_accept_prob=target_accept_prob, find_reasonable_step_size=find_reasonable_ss) rng_key_hmc, rng_key_wa = random.split(rng_key) wa_state = wa_init(z, rng_key_wa, step_size, inverse_mass_matrix=inverse_mass_matrix, mass_matrix_size=np.size(z_flat)) r = momentum_generator(wa_state.mass_matrix_sqrt, rng_key) vv_state = vv_init(z, r) energy = kinetic_fn(wa_state.inverse_mass_matrix, vv_state.r) hmc_state = HMCState(0, vv_state.z, vv_state.z_grad, vv_state.potential_energy, energy, 0, 0., 0., False, wa_state, rng_key_hmc) return hmc_state def _hmc_next(step_size, inverse_mass_matrix, vv_state, rng_key): num_steps = _get_num_steps(step_size, trajectory_len) vv_state_new = fori_loop(0, num_steps, lambda i, val: vv_update(step_size, inverse_mass_matrix, val), vv_state) energy_old = vv_state.potential_energy + kinetic_fn(inverse_mass_matrix, vv_state.r) energy_new = vv_state_new.potential_energy + kinetic_fn(inverse_mass_matrix, vv_state_new.r) delta_energy = energy_new - energy_old delta_energy = np.where(np.isnan(delta_energy), np.inf, delta_energy) accept_prob = np.clip(np.exp(-delta_energy), a_max=1.0) diverging = delta_energy > max_delta_energy transition = random.bernoulli(rng_key, accept_prob) vv_state, energy = cond(transition, (vv_state_new, energy_new), lambda args: args, (vv_state, energy_old), lambda args: args) return vv_state, energy, num_steps, accept_prob, diverging def _nuts_next(step_size, inverse_mass_matrix, vv_state, rng_key): binary_tree = build_tree(vv_update, kinetic_fn, vv_state, inverse_mass_matrix, step_size, rng_key, max_delta_energy=max_delta_energy, max_tree_depth=max_treedepth) accept_prob = binary_tree.sum_accept_probs / binary_tree.num_proposals num_steps = binary_tree.num_proposals vv_state = IntegratorState(z=binary_tree.z_proposal, r=vv_state.r, potential_energy=binary_tree.z_proposal_pe, z_grad=binary_tree.z_proposal_grad) return vv_state, binary_tree.z_proposal_energy, num_steps, accept_prob, binary_tree.diverging _next = _nuts_next if algo == 'NUTS' else _hmc_next def sample_kernel(hmc_state): """ Given an existing :data:`~numpyro.infer.mcmc.HMCState`, run HMC with fixed (possibly adapted) step size and return a new :data:`~numpyro.infer.mcmc.HMCState`. :param hmc_state: Current sample (and associated state). :return: new proposed :data:`~numpyro.infer.mcmc.HMCState` from simulating Hamiltonian dynamics given existing state. """ rng_key, rng_key_momentum, rng_key_transition = random.split(hmc_state.rng_key, 3) r = momentum_generator(hmc_state.adapt_state.mass_matrix_sqrt, rng_key_momentum) vv_state = IntegratorState(hmc_state.z, r, hmc_state.potential_energy, hmc_state.z_grad) vv_state, energy, num_steps, accept_prob, diverging = _next(hmc_state.adapt_state.step_size, hmc_state.adapt_state.inverse_mass_matrix, vv_state, rng_key_transition) # not update adapt_state after warmup phase adapt_state = cond(hmc_state.i < wa_steps, (hmc_state.i, accept_prob, vv_state.z, hmc_state.adapt_state), lambda args: wa_update(*args), hmc_state.adapt_state, lambda x: x) itr = hmc_state.i + 1 n = np.where(hmc_state.i < wa_steps, itr, itr - wa_steps) mean_accept_prob = hmc_state.mean_accept_prob + (accept_prob - hmc_state.mean_accept_prob) / n return HMCState(itr, vv_state.z, vv_state.z_grad, vv_state.potential_energy, energy, num_steps, accept_prob, mean_accept_prob, diverging, adapt_state, rng_key) # Make `init_kernel` and `sample_kernel` visible from the global scope once # `hmc` is called for sphinx doc generation. if 'SPHINX_BUILD' in os.environ: hmc.init_kernel = init_kernel hmc.sample_kernel = sample_kernel return init_kernel, sample_kernel
class MCMCKernel(ABC): """ Defines the interface for the Markov transition kernel that is used for :class:`~numpyro.infer.MCMC` inference. :param random.PRNGKey rng_key: Random number generator key to initialize the kernel. :param int num_warmup: Number of warmup steps. This can be useful when doing adaptation during warmup. :param tuple init_params: Initial parameters to begin sampling. The type must be consistent with the input type to `potential_fn`. :param model_args: Arguments provided to the model. :param model_kwargs: Keyword arguments provided to the model. """ @abstractmethod def init(self, rng_key, num_warmup, init_params, model_args, model_kwargs): raise NotImplementedError @abstractmethod def sample(self, state): """ Given the current `state`, return the next `state` using the given transition kernel. :param state: Arbitrary data structure representing the state for the kernel. For HMC, this is given by :data:`~numpyro.infer.mcmc.HMCState`. :return: Next `state`. """ raise NotImplementedError
[docs]class HMC(MCMCKernel): """ Hamiltonian Monte Carlo inference, using fixed trajectory length, with provision for step size and mass matrix adaptation. **References:** 1. *MCMC Using Hamiltonian Dynamics*, Radford M. Neal :param model: Python callable containing Pyro :mod:`~numpyro.primitives`. If model is provided, `potential_fn` will be inferred using the model. :param potential_fn: Python callable that computes the potential energy given input parameters. The input parameters to `potential_fn` can be any python collection type, provided that `init_params` argument to `init_kernel` has the same type. :param kinetic_fn: Python callable that returns the kinetic energy given inverse mass matrix and momentum. If not provided, the default is euclidean kinetic energy. :param float step_size: Determines the size of a single step taken by the verlet integrator while computing the trajectory using Hamiltonian dynamics. If not specified, it will be set to 1. :param bool adapt_step_size: A flag to decide if we want to adapt step_size during warm-up phase using Dual Averaging scheme. :param bool adapt_mass_matrix: A flag to decide if we want to adapt mass matrix during warm-up phase using Welford scheme. :param bool dense_mass: A flag to decide if mass matrix is dense or diagonal (default when ``dense_mass=False``) :param float target_accept_prob: Target acceptance probability for step size adaptation using Dual Averaging. Increasing this value will lead to a smaller step size, hence the sampling will be slower but more robust. Default to 0.8. :param float trajectory_length: Length of a MCMC trajectory for HMC. Default value is :math:`2\\pi`. :param callable init_strategy: a per-site initialization function. See :ref:`init_strategy` section for available functions. """ def __init__(self, model=None, potential_fn=None, kinetic_fn=None, step_size=1.0, adapt_step_size=True, adapt_mass_matrix=True, dense_mass=False, target_accept_prob=0.8, trajectory_length=2 * math.pi, init_strategy=init_to_uniform()): if not (model is None) ^ (potential_fn is None): raise ValueError('Only one of `model` or `potential_fn` must be specified.') self.model = model self.potential_fn = potential_fn self.kinetic_fn = kinetic_fn if kinetic_fn is not None else euclidean_kinetic_energy self.step_size = step_size self.adapt_step_size = adapt_step_size self.adapt_mass_matrix = adapt_mass_matrix self.dense_mass = dense_mass self.target_accept_prob = target_accept_prob self.trajectory_length = trajectory_length self._sample_fn = None self.algo = 'HMC' self.max_tree_depth = 10 self.init_strategy = init_strategy
[docs] @copy_docs_from(MCMCKernel.init) def init(self, rng_key, num_warmup, init_params=None, model_args=(), model_kwargs={}): constrain_fn = None if self.model is not None: if rng_key.ndim == 1: rng_key, rng_key_init_model = random.split(rng_key) else: rng_key, rng_key_init_model = np.swapaxes(vmap(random.split)(rng_key), 0, 1) init_params_, self.potential_fn, constrain_fn = initialize_model( rng_key_init_model, self.model, *model_args, init_strategy=self.init_strategy, **model_kwargs) if init_params is None: init_params = init_params_ else: # User needs to provide valid `init_params` if using `potential_fn`. if init_params is None: raise ValueError('Valid value of `init_params` must be provided with' ' `potential_fn`.') hmc_init, sample_fn = hmc(self.potential_fn, self.kinetic_fn, algo=self.algo) hmc_init_fn = lambda init_params, rng_key: hmc_init( # noqa: E731 init_params, num_warmup=num_warmup, step_size=self.step_size, adapt_step_size=self.adapt_step_size, adapt_mass_matrix=self.adapt_mass_matrix, dense_mass=self.dense_mass, target_accept_prob=self.target_accept_prob, trajectory_length=self.trajectory_length, max_tree_depth=self.max_tree_depth, rng_key=rng_key, ) if rng_key.ndim == 1: init_state = hmc_init_fn(init_params, rng_key) else: # XXX it is safe to run hmc_init_fn under vmap despite that hmc_init_fn changes some # nonlocal variables: momentum_generator, wa_update, trajectory_len, max_treedepth, # wa_steps because those variables do not depend on traced args: init_params, rng_key. init_state = vmap(hmc_init_fn)(init_params, rng_key) sample_fn = vmap(sample_fn) self._sample_fn = sample_fn return init_state, sample_fn, constrain_fn
[docs] def sample(self, state): """ Run HMC from the given :data:`~numpyro.infer.mcmc.HMCState` and return the resulting :data:`~numpyro.infer.mcmc.HMCState`. :param HMCState state: Represents the current state. :return: Next `state` after running HMC. """ # TODO: depricate this method return self._sample_fn(state)
[docs]class NUTS(HMC): """ Hamiltonian Monte Carlo inference, using the No U-Turn Sampler (NUTS) with adaptive path length and mass matrix adaptation. **References:** 1. *MCMC Using Hamiltonian Dynamics*, Radford M. Neal 2. *The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo*, Matthew D. Hoffman, and Andrew Gelman. 3. *A Conceptual Introduction to Hamiltonian Monte Carlo`*, Michael Betancourt :param model: Python callable containing Pyro :mod:`~numpyro.primitives`. If model is provided, `potential_fn` will be inferred using the model. :param potential_fn: Python callable that computes the potential energy given input parameters. The input parameters to `potential_fn` can be any python collection type, provided that `init_params` argument to `init_kernel` has the same type. :param kinetic_fn: Python callable that returns the kinetic energy given inverse mass matrix and momentum. If not provided, the default is euclidean kinetic energy. :param float step_size: Determines the size of a single step taken by the verlet integrator while computing the trajectory using Hamiltonian dynamics. If not specified, it will be set to 1. :param bool adapt_step_size: A flag to decide if we want to adapt step_size during warm-up phase using Dual Averaging scheme. :param bool adapt_mass_matrix: A flag to decide if we want to adapt mass matrix during warm-up phase using Welford scheme. :param bool dense_mass: A flag to decide if mass matrix is dense or diagonal (default when ``dense_mass=False``) :param float target_accept_prob: Target acceptance probability for step size adaptation using Dual Averaging. Increasing this value will lead to a smaller step size, hence the sampling will be slower but more robust. Default to 0.8. :param float trajectory_length: Length of a MCMC trajectory for HMC. This arg has no effect in NUTS sampler. :param int max_tree_depth: Max depth of the binary tree created during the doubling scheme of NUTS sampler. Defaults to 10. :param callable init_strategy: a per-site initialization function. See :ref:`init_strategy` section for available functions. """ def __init__(self, model=None, potential_fn=None, kinetic_fn=None, step_size=1.0, adapt_step_size=True, adapt_mass_matrix=True, dense_mass=False, target_accept_prob=0.8, trajectory_length=None, max_tree_depth=10, init_strategy=init_to_uniform()): super(NUTS, self).__init__(potential_fn=potential_fn, model=model, kinetic_fn=kinetic_fn, step_size=step_size, adapt_step_size=adapt_step_size, adapt_mass_matrix=adapt_mass_matrix, dense_mass=dense_mass, target_accept_prob=target_accept_prob, trajectory_length=trajectory_length, init_strategy=init_strategy) self.max_tree_depth = max_tree_depth self.algo = 'NUTS'
def _laxmap(f, xs): n = tree_flatten(xs)[0][0].shape[0] def get_value_from_index(i): return tree_map(lambda x: x[i], xs) ys = [] for i in range(n): x = jit(get_value_from_index)(i) ys.append(f(x)) return tree_multimap(lambda *args: np.stack(args), *ys)
[docs]class MCMC(object): """ Provides access to Markov Chain Monte Carlo inference algorithms in NumPyro. .. note:: `chain_method` is an experimental arg, which might be removed in a future version. .. note:: Setting `progress_bar=False` will improve the speed for many cases. :param MCMCKernel sampler: an instance of :class:`~numpyro.infer.mcmc.MCMCKernel` that determines the sampler for running MCMC. Currently, only :class:`~numpyro.infer.mcmc.HMC` and :class:`~numpyro.infer.mcmc.NUTS` are available. :param int num_warmup: Number of warmup steps. :param int num_samples: Number of samples to generate from the Markov chain. :param int num_chains: Number of Number of MCMC chains to run. By default, chains will be run in parallel using :func:`jax.pmap`, failing which, chains will be run in sequence. :param constrain_fn: Callable that converts a collection of unconstrained sample values returned from the sampler to constrained values that lie within the support of the sample sites. :param str chain_method: One of 'parallel' (default), 'sequential', 'vectorized'. The method 'parallel' is used to execute the drawing process in parallel on XLA devices (CPUs/GPUs/TPUs), If there are not enough devices for 'parallel', we fall back to 'sequential' method to draw chains sequentially. 'vectorized' method is an experimental feature which vectorizes the drawing method, hence allowing us to collect samples in parallel on a single device. :param bool progress_bar: Whether to enable progress bar updates. Defaults to ``True``. """ def __init__(self, sampler, num_warmup, num_samples, num_chains=1, constrain_fn=None, chain_method='parallel', progress_bar=True): self.sampler = sampler self.num_warmup = num_warmup self.num_samples = num_samples self.num_chains = num_chains self.constrain_fn = constrain_fn self.chain_method = chain_method self.progress_bar = progress_bar # TODO: We should have progress bars (maybe without diagnostics) for num_chains > 1 if (chain_method == 'parallel' and num_chains > 1) or ( "CI" in os.environ or "PYTEST_XDIST_WORKER" in os.environ): self.progress_bar = False self._states = None self._states_flat = None def _single_chain_mcmc(self, init, collect_fields=('z',), collect_warmup=False, args=(), kwargs={}): rng_key, init_params = init init_state, sample_fn, constrain_fn = self.sampler.init(rng_key, self.num_warmup, init_params, model_args=args, model_kwargs=kwargs) if self.constrain_fn is None: constrain_fn = identity if constrain_fn is None else constrain_fn else: constrain_fn = self.constrain_fn collect_fn = attrgetter(*collect_fields) lower = 0 if collect_warmup else self.num_warmup states = fori_collect(lower, self.num_warmup + self.num_samples, sample_fn, init_state, transform=collect_fn, progbar=self.progress_bar, progbar_desc=functools.partial(get_progbar_desc_str, self.num_warmup), diagnostics_fn=get_diagnostics_str if rng_key.ndim == 1 else None) if len(collect_fields) == 1: states = (states,) states = dict(zip(collect_fields, states)) states['z'] = vmap(constrain_fn)(states['z']) if len(tree_flatten(states['z'])[0]) > 0 else states['z'] return states
[docs] def run(self, rng_key, *args, extra_fields=(), collect_warmup=False, init_params=None, **kwargs): """ Run the MCMC samplers and collect samples. :param random.PRNGKey rng_key: Random number generator key to be used for the sampling. :param args: Arguments to be provided to the :meth:`numpyro.infer.mcmc.MCMCKernel.init` method. These are typically the arguments needed by the `model`. :param extra_fields: Extra fields (aside from `z`, `diverging`) from :data:`numpyro.infer.mcmc.HMCState` to collect during the MCMC run. :type extra_fields: tuple or list :param bool collect_warmup: Whether to collect samples from the warmup phase. Defaults to `False`. :param init_params: Initial parameters to begin sampling. The type must be consistent with the input type to `potential_fn`. :param kwargs: Keyword arguments to be provided to the :meth:`numpyro.infer.mcmc.MCMCKernel.init` method. These are typically the keyword arguments needed by the `model`. """ self._args = args self._kwargs = kwargs chain_method = self.chain_method if chain_method == 'parallel' and xla_bridge.device_count() < self.num_chains: chain_method = 'sequential' warnings.warn('There are not enough devices to run parallel chains: expected {} but got {}.' ' Chains will be drawn sequentially. If you are running MCMC in CPU,' ' consider to use `numpyro.set_host_device_count({})` at the beginning' ' of your program.' .format(self.num_chains, xla_bridge.device_count(), self.num_chains)) if init_params is not None and self.num_chains > 1: prototype_init_val = tree_flatten(init_params)[0][0] if np.shape(prototype_init_val)[0] != self.num_chains: raise ValueError('`init_params` must have the same leading dimension' ' as `num_chains`.') assert isinstance(extra_fields, (tuple, list)) collect_fields = tuple(set(('z', 'diverging') + tuple(extra_fields))) if self.num_chains == 1: states_flat = self._single_chain_mcmc((rng_key, init_params), collect_fields, collect_warmup, args, kwargs) states = tree_map(lambda x: x[np.newaxis, ...], states_flat) else: rng_keys = random.split(rng_key, self.num_chains) partial_map_fn = partial(self._single_chain_mcmc, collect_fields=collect_fields, collect_warmup=collect_warmup, args=args, kwargs=kwargs) if chain_method == 'sequential': if self.progress_bar: map_fn = partial(_laxmap, partial_map_fn) else: map_fn = partial(lax.map, partial_map_fn) elif chain_method == 'parallel': map_fn = pmap(partial_map_fn) elif chain_method == 'vectorized': map_fn = partial_map_fn else: raise ValueError('Only supporting the following methods to draw chains:' ' "sequential", "parallel", or "vectorized"') states = map_fn((rng_keys, init_params)) if chain_method == 'vectorized': # swap num_samples x num_chains to num_chains x num_samples states = tree_map(lambda x: np.swapaxes(x, 0, 1), states) states_flat = tree_map(lambda x: np.reshape(x, (-1,) + x.shape[2:]), states) self._states = states self._states_flat = states_flat
[docs] def get_samples(self, group_by_chain=False): """ Get samples from the MCMC run. :param bool group_by_chain: Whether to preserve the chain dimension. If True, all samples will have num_chains as the size of their leading dimension. :return: Samples having the same data type as `init_params`. The data type is a `dict` keyed on site names if a model containing Pyro primitives is used, but can be any :func:`jaxlib.pytree`, more generally (e.g. when defining a `potential_fn` for HMC that takes `list` args). """ return self._states['z'] if group_by_chain else self._states_flat['z']
[docs] def get_extra_fields(self, group_by_chain=False): """ Get extra fields from the MCMC run. :param bool group_by_chain: Whether to preserve the chain dimension. If True, all samples will have num_chains as the size of their leading dimension. :return: Extra fields keyed by field names which are specified in the `extra_fields` keyword of :meth:`run`. """ states = self._states if group_by_chain else self._states_flat return {k: v for k, v in states.items() if k != 'z'}
[docs] def print_summary(self, prob=0.9): print_summary(self._states['z'], prob=prob) extra_fields = self.get_extra_fields() if 'diverging' in extra_fields: print("Number of divergences: {}".format(np.sum(extra_fields['diverging'])))