Source code for pyro.poutine.condition_messenger

# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0

from typing import TYPE_CHECKING, Dict, Union

import torch

from pyro.poutine.messenger import Messenger
from pyro.poutine.trace_struct import Trace

    from pyro.poutine.runtime import Message

[docs]class ConditionMessenger(Messenger): """ Given a stochastic function with some sample statements and a dictionary of observations at names, change the sample statements at those names into observes with those values. Consider the following Pyro program: >>> def model(x): ... s = pyro.param("s", torch.tensor(0.5)) ... z = pyro.sample("z", dist.Normal(x, s)) ... return z ** 2 To observe a value for site `z`, we can write >>> conditioned_model = pyro.poutine.condition(model, data={"z": torch.tensor(1.)}) This is equivalent to adding `obs=value` as a keyword argument to `pyro.sample("z", ...)` in `model`. :param fn: a stochastic function (callable containing Pyro primitive calls) :param data: a dict or a :class:`~pyro.poutine.Trace` :returns: stochastic function decorated with a :class:`~pyro.poutine.condition_messenger.ConditionMessenger` """ def __init__(self, data: Union[Dict[str, torch.Tensor], Trace]) -> None: """ :param data: a dict or a Trace Constructor. Doesn't do much, just stores the stochastic function and the data to condition on. """ super().__init__() = data def _pyro_sample(self, msg: "Message") -> None: """ :param msg: current message at a trace site. :returns: a sample from the stochastic function at the site. If msg["name"] appears in, convert the sample site into an observe site whose observed value is the value from[msg["name"]]. Otherwise, implements default sampling behavior with no additional effects. """ assert isinstance(msg["name"], str) name = msg["name"] if name in if isinstance(, Trace): msg["value"] =[name]["value"] else: msg["value"] =[name] msg["is_observed"] = msg["value"] is not None