Source code for pyro.infer.predictive

from functools import reduce
import warnings

import torch

import pyro
import pyro.poutine as poutine
from pyro.poutine.util import prune_subsample_sites

def _guess_max_plate_nesting(model, args, kwargs):
    Guesses max_plate_nesting by running the model once
    without enumeration. This optimistically assumes static model
    with poutine.block():
        model_trace = poutine.trace(model).get_trace(*args, **kwargs)
    sites = [site for site in model_trace.nodes.values()
             if site["type"] == "sample"]

    dims = [frame.dim
            for site in sites
            for frame in site["cond_indep_stack"]
            if frame.vectorized]
    max_plate_nesting = -min(dims) if dims else 0
    return max_plate_nesting

def _predictive_sequential(model, posterior_samples, model_args, model_kwargs,
                           num_samples, return_site_shapes, return_trace=False):
    collected = []
    samples = [{k: v[i] for k, v in posterior_samples.items()} for i in range(num_samples)]
    for i in range(num_samples):
        trace = poutine.trace(poutine.condition(model, samples[i])).get_trace(*model_args, **model_kwargs)
        if return_trace:
            collected.append({site: trace.nodes[site]['value'] for site in return_site_shapes})

    if return_trace:
        return collected
        return {site: torch.stack([s[site] for s in collected]).reshape(shape)
                for site, shape in return_site_shapes.items()}

def _predictive(model, posterior_samples, num_samples, return_sites=(),
                return_trace=False, parallel=False, model_args=(), model_kwargs={}):
    max_plate_nesting = _guess_max_plate_nesting(model, model_args, model_kwargs)
    vectorize = pyro.plate("_num_predictive_samples", num_samples, dim=-max_plate_nesting-1)
    model_trace = prune_subsample_sites(poutine.trace(model).get_trace(*model_args, **model_kwargs))
    reshaped_samples = {}

    for name, sample in posterior_samples.items():
        sample_shape = sample.shape[1:]
        sample = sample.reshape((num_samples,) + (1,) * (max_plate_nesting - len(sample_shape)) + sample_shape)
        reshaped_samples[name] = sample

    if return_trace:
        trace = poutine.trace(poutine.condition(vectorize(model), reshaped_samples))\
            .get_trace(*model_args, **model_kwargs)
        return trace

    return_site_shapes = {}
    for site in model_trace.stochastic_nodes + model_trace.observation_nodes:
        append_ndim = max_plate_nesting - len(model_trace.nodes[site]["fn"].batch_shape)
        site_shape = (num_samples,) + (1,) * append_ndim + model_trace.nodes[site]['value'].shape
        # non-empty return-sites
        if return_sites:
            if site in return_sites:
                return_site_shapes[site] = site_shape
        # special case (for guides): include all sites
        elif return_sites is None:
            return_site_shapes[site] = site_shape
        # default case: return sites = ()
        # include all sites not in posterior samples
        elif site not in posterior_samples:
            return_site_shapes[site] = site_shape

    # handle _RETURN site
    if return_sites is not None and '_RETURN' in return_sites:
        value = model_trace.nodes['_RETURN']['value']
        shape = (num_samples,) + value.shape if torch.is_tensor(value) else None
        return_site_shapes['_RETURN'] = shape

    if not parallel:
        return _predictive_sequential(model, posterior_samples, model_args, model_kwargs, num_samples,
                                      return_site_shapes, return_trace=False)

    trace = poutine.trace(poutine.condition(vectorize(model), reshaped_samples))\
        .get_trace(*model_args, **model_kwargs)
    predictions = {}
    for site, shape in return_site_shapes.items():
        value = trace.nodes[site]['value']
        if site == '_RETURN' and shape is None:
            predictions[site] = value
        if value.numel() < reduce((lambda x, y: x * y), shape):
            predictions[site] = value.expand(shape)
            predictions[site] = value.reshape(shape)

    return predictions

[docs]class Predictive(torch.nn.Module): """ EXPERIMENTAL class used to construct predictive distribution. The predictive distribution is obtained by running the `model` conditioned on latent samples from `posterior_samples`. If a `guide` is provided, then posterior samples from all the latent sites are also returned. .. warning:: The interface for the :class:`Predictive` class is experimental, and might change in the future. :param model: Python callable containing Pyro primitives. :param dict posterior_samples: dictionary of samples from the posterior. :param callable guide: optional guide to get posterior samples of sites not present in `posterior_samples`. :param int num_samples: number of samples to draw from the predictive distribution. This argument has no effect if ``posterior_samples`` is non-empty, in which case, the leading dimension size of samples in ``posterior_samples`` is used. :param return_sites: sites to return; by default only sample sites not present in `posterior_samples` are returned. :type return_sites: list, tuple, or set :param bool parallel: predict in parallel by wrapping the existing model in an outermost `plate` messenger. Note that this requires that the model has all batch dims correctly annotated via :class:`~pyro.plate`. Default is `False`. """ def __init__(self, model, posterior_samples=None, guide=None, num_samples=None, return_sites=(), parallel=False): super().__init__() if posterior_samples is None: if num_samples is None: raise ValueError("Either posterior_samples or num_samples must be specified.") posterior_samples = {} for name, sample in posterior_samples.items(): batch_size = sample.shape[0] if num_samples is None: num_samples = batch_size elif num_samples != batch_size: warnings.warn("Sample's leading dimension size {} is different from the " "provided {} num_samples argument. Defaulting to {}." .format(batch_size, num_samples, batch_size), UserWarning) num_samples = batch_size if num_samples is None: raise ValueError("No sample sites in posterior samples to infer `num_samples`.") if guide is not None and posterior_samples: raise ValueError("`posterior_samples` cannot be provided with the `guide` argument.") if return_sites is not None: assert isinstance(return_sites, (list, tuple, set)) self.model = model self.posterior_samples = {} if posterior_samples is None else posterior_samples self.num_samples = num_samples = guide self.return_sites = return_sites self.parallel = parallel
[docs] def call(self, *args, **kwargs): """ Method that calls :meth:`forward` and returns parameter values of the guide as a `tuple` instead of a `dict`, which is a requirement for JIT tracing. Unlike :meth:`forward`, this method can be traced by :func:`torch.jit.trace_module`. .. warning:: This method may be removed once PyTorch JIT tracer starts accepting `dict` as valid return types. See `issue <>`_. """ result = self.forward(*args, **kwargs) return tuple(v for _, v in sorted(result.items()))
[docs] def forward(self, *args, **kwargs): """ Returns dict of samples from the predictive distribution. By default, only sample sites not contained in `posterior_samples` are returned. This can be modified by changing the `return_sites` keyword argument of this :class:`Predictive` instance. :param args: model arguments. :param kwargs: model keyword arguments. """ posterior_samples = self.posterior_samples return_sites = self.return_sites if is not None: # return all sites by default if a guide is provided. return_sites = None if not return_sites else return_sites posterior_samples = _predictive(, posterior_samples, self.num_samples, return_sites=None, parallel=self.parallel, model_args=args, model_kwargs=kwargs) return _predictive(self.model, posterior_samples, self.num_samples, return_sites=return_sites, parallel=self.parallel, model_args=args, model_kwargs=kwargs)
[docs] def get_samples(self, *args, **kwargs): warnings.warn("The method `.get_samples` has been deprecated in favor of `.forward`.", DeprecationWarning) return self.forward(*args, **kwargs)
[docs] def get_vectorized_trace(self, *args, **kwargs): """ Returns a single vectorized `trace` from the predictive distribution. Note that this requires that the model has all batch dims correctly annotated via :class:`~pyro.plate`. :param args: model arguments. :param kwargs: model keyword arguments. """ posterior_samples = self.posterior_samples if is not None: posterior_samples = _predictive(, posterior_samples, self.num_samples, parallel=self.parallel, model_args=args, model_kwargs=kwargs) return _predictive(self.model, posterior_samples, self.num_samples, return_trace=True, model_args=args, model_kwargs=kwargs)