# Copyright Contributors to the Pyro project. # SPDX-License-Identifier: Apache-2.0 """ This provides a small set of effect handlers in NumPyro that are modeled after Pyro's `poutine `_ module. For a tutorial on effect handlers more generally, readers are encouraged to read `Poutine: A Guide to Programming with Effect Handlers in Pyro `_. These simple effect handlers can be composed together or new ones added to enable implementation of custom inference utilities and algorithms. **Example** As an example, we are using :class:`~numpyro.handlers.seed`, :class:`~numpyro.handlers.trace` and :class:`~numpyro.handlers.substitute` handlers to define the `log_likelihood` function below. We first create a logistic regression model and sample from the posterior distribution over the regression parameters using :func:`~numpyro.infer.MCMC`. The `log_likelihood` function uses effect handlers to run the model by substituting sample sites with values from the posterior distribution and computes the log density for a single data point. The `log_predictive_density` function computes the log likelihood for each draw from the joint posterior and aggregates the results for all the data points, but does so by using JAX's auto-vectorize transform called `vmap` so that we do not need to loop over all the data points. .. doctest:: >>> import jax.numpy as jnp >>> from jax import random, vmap >>> from jax.scipy.special import logsumexp >>> import numpyro >>> import numpyro.distributions as dist >>> from numpyro import handlers >>> from numpyro.infer import MCMC, NUTS >>> N, D = 3000, 3 >>> def logistic_regression(data, labels): ... coefs = numpyro.sample('coefs', dist.Normal(jnp.zeros(D), jnp.ones(D))) ... intercept = numpyro.sample('intercept', dist.Normal(0., 10.)) ... logits = jnp.sum(coefs * data + intercept, axis=-1) ... return numpyro.sample('obs', dist.Bernoulli(logits=logits), obs=labels) >>> data = random.normal(random.PRNGKey(0), (N, D)) >>> true_coefs = jnp.arange(1., D + 1.) >>> logits = jnp.sum(true_coefs * data, axis=-1) >>> labels = dist.Bernoulli(logits=logits).sample(random.PRNGKey(1)) >>> num_warmup, num_samples = 1000, 1000 >>> mcmc = MCMC(NUTS(model=logistic_regression), num_warmup=num_warmup, num_samples=num_samples) >>> mcmc.run(random.PRNGKey(2), data, labels) # doctest: +SKIP sample: 100%|██████████| 1000/1000 [00:00<00:00, 1252.39it/s, 1 steps of size 5.83e-01. acc. prob=0.85] >>> mcmc.print_summary() # doctest: +SKIP mean sd 5.5% 94.5% n_eff Rhat coefs[0] 0.96 0.07 0.85 1.07 455.35 1.01 coefs[1] 2.05 0.09 1.91 2.20 332.00 1.01 coefs[2] 3.18 0.13 2.96 3.37 320.27 1.00 intercept -0.03 0.02 -0.06 0.00 402.53 1.00 >>> def log_likelihood(rng_key, params, model, *args, **kwargs): ... model = handlers.substitute(handlers.seed(model, rng_key), params) ... model_trace = handlers.trace(model).get_trace(*args, **kwargs) ... obs_node = model_trace['obs'] ... return obs_node['fn'].log_prob(obs_node['value']) >>> def log_predictive_density(rng_key, params, model, *args, **kwargs): ... n = list(params.values())[0].shape[0] ... log_lk_fn = vmap(lambda rng_key, params: log_likelihood(rng_key, params, model, *args, **kwargs)) ... log_lk_vals = log_lk_fn(random.split(rng_key, n), params) ... return jnp.sum(logsumexp(log_lk_vals, 0) - jnp.log(n)) >>> print(log_predictive_density(random.PRNGKey(2), mcmc.get_samples(), ... logistic_regression, data, labels)) # doctest: +SKIP -874.89813 """ from collections import OrderedDict import warnings import numpy as np from jax import random import jax.numpy as jnp import numpyro from numpyro.distributions.distribution import COERCIONS from numpyro.primitives import ( _PYRO_STACK, CondIndepStackFrame, Messenger, apply_stack, plate, ) from numpyro.util import find_stack_level, not_jax_tracer __all__ = [ "block", "collapse", "condition", "infer_config", "lift", "mask", "reparam", "replay", "scale", "scope", "seed", "substitute", "trace", "do", ] class trace(Messenger): """ Returns a handler that records the inputs and outputs at primitive calls inside `fn`. **Example:** .. doctest:: >>> from jax import random >>> import numpyro >>> import numpyro.distributions as dist >>> from numpyro.handlers import seed, trace >>> import pprint as pp >>> def model(): ... numpyro.sample('a', dist.Normal(0., 1.)) >>> exec_trace = trace(seed(model, random.PRNGKey(0))).get_trace() >>> pp.pprint(exec_trace) # doctest: +SKIP OrderedDict([('a', {'args': (), 'fn': , 'is_observed': False, 'kwargs': {'rng_key': DeviceArray([0, 0], dtype=uint32)}, 'name': 'a', 'type': 'sample', 'value': DeviceArray(-0.20584235, dtype=float32)})]) """ def __enter__(self): super(trace, self).__enter__() self.trace = OrderedDict() return self.trace def postprocess_message(self, msg): if "name" not in msg: # skip recording helper messages e.g. `control_flow`, `to_data`, `to_funsor` # which has no name return assert not ( msg["type"] in ("sample", "deterministic") and msg["name"] in self.trace ), "all sites must have unique names but got `{}` duplicated".format( msg["name"] ) self.trace[msg["name"]] = msg.copy() def get_trace(self, *args, **kwargs): """ Run the wrapped callable and return the recorded trace. :param `*args`: arguments to the callable. :param `**kwargs`: keyword arguments to the callable. :return: `OrderedDict` containing the execution trace. """ self(*args, **kwargs) return self.trace class replay(Messenger): """ Given a callable `fn` and an execution trace `trace`, return a callable which substitutes `sample` calls in `fn` with values from the corresponding site names in `trace`. :param fn: Python callable with NumPyro primitives. :param trace: an OrderedDict containing execution metadata. **Example:** .. doctest:: >>> from jax import random >>> import numpyro >>> import numpyro.distributions as dist >>> from numpyro.handlers import replay, seed, trace >>> def model(): ... numpyro.sample('a', dist.Normal(0., 1.)) >>> exec_trace = trace(seed(model, random.PRNGKey(0))).get_trace() >>> print(exec_trace['a']['value']) # doctest: +SKIP -0.20584235 >>> replayed_trace = trace(replay(model, exec_trace)).get_trace() >>> print(exec_trace['a']['value']) # doctest: +SKIP -0.20584235 >>> assert replayed_trace['a']['value'] == exec_trace['a']['value'] """ def __init__(self, fn=None, trace=None): assert trace is not None self.trace = trace super(replay, self).__init__(fn) def process_message(self, msg): if msg["type"] in ("sample", "plate") and msg["name"] in self.trace: name = msg["name"] if msg["type"] in ("sample", "plate") and name in self.trace: guide_msg = self.trace[name] if msg["type"] == "plate": if guide_msg["type"] != "plate": raise RuntimeError(f"Site {name} must be a plate in trace.") msg["value"] = guide_msg["value"] return None if msg["is_observed"]: return None if guide_msg["type"] != "sample" or guide_msg["is_observed"]: raise RuntimeError(f"Site {name} must be sampled in trace.") msg["value"] = guide_msg["value"] msg["infer"] = guide_msg["infer"] class block(Messenger): """ Given a callable `fn`, return another callable that selectively hides primitive sites where `hide_fn` returns True from other effect handlers on the stack. :param callable fn: Python callable with NumPyro primitives. :param callable hide_fn: function which when given a dictionary containing site-level metadata returns whether it should be blocked. :param list hide: list of site names to hide. :param list expose_types: list of site types to expose, e.g. `['param']`. **Example:** .. doctest:: >>> from jax import random >>> import numpyro >>> from numpyro.handlers import block, seed, trace >>> import numpyro.distributions as dist >>> def model(): ... a = numpyro.sample('a', dist.Normal(0., 1.)) ... return numpyro.sample('b', dist.Normal(a, 1.)) >>> model = seed(model, random.PRNGKey(0)) >>> block_all = block(model) >>> block_a = block(model, lambda site: site['name'] == 'a') >>> trace_block_all = trace(block_all).get_trace() >>> assert not {'a', 'b'}.intersection(trace_block_all.keys()) >>> trace_block_a = trace(block_a).get_trace() >>> assert 'a' not in trace_block_a >>> assert 'b' in trace_block_a """ def __init__(self, fn=None, hide_fn=None, hide=None, expose_types=None): if hide_fn is not None: self.hide_fn = hide_fn elif hide is not None: self.hide_fn = lambda msg: msg.get("name") in hide elif expose_types is not None: self.hide_fn = lambda msg: msg.get("type") not in expose_types else: self.hide_fn = lambda msg: True super(block, self).__init__(fn) def process_message(self, msg): if self.hide_fn(msg): msg["stop"] = True class collapse(trace): """ EXPERIMENTAL Collapses all sites in the context by lazily sampling and attempting to use conjugacy relations. If no conjugacy is known this will fail. Code using the results of sample sites must be written to accept Funsors rather than Tensors. This requires ``funsor`` to be installed. """ _coerce = None def __init__(self, *args, **kwargs): if collapse._coerce is None: import funsor from funsor.distribution import CoerceDistributionToFunsor funsor.set_backend("jax") collapse._coerce = CoerceDistributionToFunsor("jax") super().__init__(*args, **kwargs) def process_message(self, msg): from funsor.terms import Funsor if msg["type"] == "sample": if msg["value"] is None: msg["value"] = msg["name"] if isinstance(msg["fn"], Funsor) or isinstance(msg["value"], (str, Funsor)): msg["stop"] = True def __enter__(self): self.preserved_plates = frozenset( h.name for h in _PYRO_STACK if isinstance(h, plate) ) COERCIONS.append(self._coerce) return super().__enter__() def __exit__(self, exc_type, exc_value, traceback): import funsor _coerce = COERCIONS.pop() assert _coerce is self._coerce super().__exit__(exc_type, exc_value, traceback) if exc_type is not None: return # Convert delayed statements to pyro.factor() reduced_vars = [] log_prob_terms = [] plates = frozenset() for name, site in self.trace.items(): if site["type"] != "sample": continue if not site["is_observed"]: reduced_vars.append(name) dim_to_name = {f.dim: f.name for f in site["cond_indep_stack"]} fn = funsor.to_funsor(site["fn"], funsor.Real, dim_to_name) value = site["value"] if not isinstance(value, str): value = funsor.to_funsor(site["value"], fn.inputs["value"], dim_to_name) log_prob_terms.append(fn(value=value)) plates |= frozenset(f.name for f in site["cond_indep_stack"]) assert log_prob_terms, "nothing to collapse" reduced_plates = plates - self.preserved_plates log_prob = funsor.sum_product.sum_product( funsor.ops.logaddexp, funsor.ops.add, log_prob_terms, eliminate=frozenset(reduced_vars) | reduced_plates, plates=plates, ) name = reduced_vars[0] numpyro.factor(name, log_prob.data) class condition(Messenger): """ Conditions unobserved sample sites to values from `data` or `condition_fn`. Similar to :class:`~numpyro.handlers.substitute` except that it only affects `sample` sites and changes the `is_observed` property to `True`. :param fn: Python callable with NumPyro primitives. :param dict data: dictionary of `numpy.ndarray` values keyed by site names. :param condition_fn: callable that takes in a site dict and returns a numpy array or `None` (in which case the handler has no side effect). **Example:** .. doctest:: >>> from jax import random >>> import numpyro >>> from numpyro.handlers import condition, seed, substitute, trace >>> import numpyro.distributions as dist >>> def model(): ... numpyro.sample('a', dist.Normal(0., 1.)) >>> model = seed(model, random.PRNGKey(0)) >>> exec_trace = trace(condition(model, {'a': -1})).get_trace() >>> assert exec_trace['a']['value'] == -1 >>> assert exec_trace['a']['is_observed'] """ def __init__(self, fn=None, data=None, condition_fn=None): self.condition_fn = condition_fn self.data = data if sum((x is not None for x in (data, condition_fn))) != 1: raise ValueError( "Only one of `data` or `condition_fn` " "should be provided." ) super(condition, self).__init__(fn) def process_message(self, msg): if (msg["type"] != "sample") or msg.get("_control_flow_done", False): if msg["type"] == "control_flow": if self.data is not None: msg["kwargs"]["substitute_stack"].append(("condition", self.data)) if self.condition_fn is not None: msg["kwargs"]["substitute_stack"].append( ("condition", self.condition_fn) ) return if self.data is not None: value = self.data.get(msg["name"]) else: value = self.condition_fn(msg) if value is not None: msg["value"] = value msg["is_observed"] = True class infer_config(Messenger): """ Given a callable `fn` that contains NumPyro primitive calls and a callable `config_fn` taking a trace site and returning a dictionary, updates the value of the infer kwarg at a sample site to config_fn(site). :param fn: a stochastic function (callable containing NumPyro primitive calls) :param config_fn: a callable taking a site and returning an infer dict """ def __init__(self, fn=None, config_fn=None): super().__init__(fn) self.config_fn = config_fn def process_message(self, msg): if msg["type"] in ("sample",): msg["infer"].update(self.config_fn(msg)) class lift(Messenger): """ Given a stochastic function with ``param`` calls and a prior distribution, create a stochastic function where all param calls are replaced by sampling from prior. Prior should be a distribution or a dict of names to distributions. Consider the following NumPyro program: >>> import numpyro >>> import numpyro.distributions as dist >>> from numpyro.handlers import lift >>> >>> def model(x): ... s = numpyro.param("s", 0.5) ... z = numpyro.sample("z", dist.Normal(x, s)) ... return z ** 2 >>> lifted_model = lift(model, prior={"s": dist.Exponential(0.3)}) ``lift`` makes ``param`` statements behave like ``sample`` statements using the distributions in ``prior``. In this example, site `s` will now behave as if it was replaced with ``s = numpyro.sample("s", dist.Exponential(0.3))``. :param fn: function whose parameters will be lifted to random values :param prior: prior function in the form of a Distribution or a dict of Distributions """ def __init__(self, fn=None, prior=None): super().__init__(fn) self.prior = prior self._samples_cache = {} def __enter__(self): self._samples_cache = {} return super().__enter__() def __exit__(self, *args, **kwargs): self._samples_cache = {} return super().__exit__(*args, **kwargs) def process_message(self, msg): if msg["type"] != "param": return name = msg["name"] fn = self.prior.get(name) if isinstance(self.prior, dict) else self.prior if isinstance(fn, numpyro.distributions.Distribution): msg["type"] = "sample" msg["fn"] = fn msg["args"] = () msg["kwargs"] = { "rng_key": msg["kwargs"].get("rng_key", None), "sample_shape": msg["kwargs"].get("sample_shape", ()), } msg["intermediates"] = [] msg["infer"] = msg.get("infer", {}) else: # otherwise leave as is return if name in self._samples_cache: # Multiple pyro.param statements with the same # name. Block the site and fix the value. msg["value"] = self._samples_cache[name]["value"] msg["is_observed"] = True msg["stop"] = True else: self._samples_cache[name] = msg msg["is_observed"] = False class mask(Messenger): """ This messenger masks out some of the sample statements elementwise. :param mask: a boolean or a boolean-valued array for masking elementwise log probability of sample sites (`True` includes a site, `False` excludes a site). """ def __init__(self, fn=None, mask=True): if jnp.result_type(mask) != "bool": raise ValueError("`mask` should be a bool array.") self.mask = mask super().__init__(fn) def process_message(self, msg): if msg["type"] != "sample": if msg["type"] == "inspect": msg["mask"] = ( self.mask if msg["mask"] is None else (self.mask & msg["mask"]) ) return msg["fn"] = msg["fn"].mask(self.mask) class reparam(Messenger): """ Reparametrizes each affected sample site into one or more auxiliary sample sites followed by a deterministic transformation [1]. To specify reparameterizers, pass a ``config`` dict or callable to the constructor. See the :mod:`numpyro.infer.reparam` module for available reparameterizers. Note some reparameterizers can examine the ``*args,**kwargs`` inputs of functions they affect; these reparameterizers require using ``handlers.reparam`` as a decorator rather than as a context manager. [1] Maria I. Gorinova, Dave Moore, Matthew D. Hoffman (2019) "Automatic Reparameterisation of Probabilistic Programs" https://arxiv.org/pdf/1906.03028.pdf :param config: Configuration, either a dict mapping site name to :class:`~numpyro.infer.reparam.Reparam` , or a function mapping site to :class:`~numpyro.infer.reparam.Reparam` or None. :type config: dict or callable """ def __init__(self, fn=None, config=None): assert isinstance(config, dict) or callable(config) self.config = config super().__init__(fn) def process_message(self, msg): if msg["type"] != "sample": return if isinstance(self.config, dict): reparam = self.config.get(msg["name"]) else: reparam = self.config(msg) if reparam is None: return new_fn, value = reparam(msg["name"], msg["fn"], msg["value"]) if value is not None: if new_fn is None: msg["type"] = "deterministic" msg["value"] = value for key in list(msg.keys()): if key not in ("type", "name", "value"): del msg[key] return if msg["value"] is None: msg["is_observed"] = True msg["value"] = value msg["fn"] = new_fn class scale(Messenger): """ This messenger rescales the log probability score. This is typically used for data subsampling or for stratified sampling of data (e.g. in fraud detection where negatives vastly outnumber positives). :param scale: a positive scaling factor that is broadcastable to the shape of log probability. :type scale: float or numpy.ndarray """ def __init__(self, fn=None, scale=1.0): if not_jax_tracer(scale): if np.any(np.less_equal(scale, 0)): raise ValueError("'scale' argument should be positive.") self.scale = scale super().__init__(fn) def process_message(self, msg): if msg["type"] not in ("param", "sample", "plate"): return msg["scale"] = ( self.scale if msg.get("scale") is None else self.scale * msg["scale"] ) class scope(Messenger): """ This handler prepend a prefix followed by a divider to the name of sample sites. **Example:** .. doctest:: >>> import numpyro >>> import numpyro.distributions as dist >>> from numpyro.handlers import scope, seed, trace >>> def model(): ... with scope(prefix="a"): ... with scope(prefix="b", divider="."): ... return numpyro.sample("x", dist.Bernoulli(0.5)) ... >>> assert "a/b.x" in trace(seed(model, 0)).get_trace() :param fn: Python callable with NumPyro primitives. :param str prefix: a string to prepend to sample names :param str divider: a string to join the prefix and sample name; default to `'/'` :param list hide_types: an optional list of side types to skip renaming. """ def __init__(self, fn=None, prefix="", divider="/", *, hide_types=None): self.prefix = prefix self.divider = divider self.hide_types = [] if hide_types is None else hide_types super().__init__(fn) def process_message(self, msg): if msg.get("name") and msg["type"] not in self.hide_types: msg["name"] = f"{self.prefix}{self.divider}{msg['name']}" if msg.get("cond_indep_stack") and "plate" not in self.hide_types: msg["cond_indep_stack"] = [ CondIndepStackFrame( f"{self.prefix}{self.divider}{i.name}", i.dim, i.size ) for i in msg["cond_indep_stack"] ] class seed(Messenger): """ JAX uses a functional pseudo random number generator that requires passing in a seed :func:`~jax.random.PRNGKey` to every stochastic function. The `seed` handler allows us to initially seed a stochastic function with a :func:`~jax.random.PRNGKey`. Every call to the :func:`~numpyro.handlers.sample` primitive inside the function results in a splitting of this initial seed so that we use a fresh seed for each subsequent call without having to explicitly pass in a `PRNGKey` to each `sample` call. :param fn: Python callable with NumPyro primitives. :param rng_seed: a random number generator seed. :type rng_seed: int, jnp.ndarray scalar, or jax.random.PRNGKey .. note:: Unlike in Pyro, `numpyro.sample` primitive cannot be used without wrapping it in seed handler since there is no global random state. As such, users need to use `seed` as a contextmanager to generate samples from distributions or as a decorator for their model callable (See below). **Example:** .. doctest:: >>> from jax import random >>> import numpyro >>> import numpyro.handlers >>> import numpyro.distributions as dist >>> # as context manager >>> with handlers.seed(rng_seed=1): ... x = numpyro.sample('x', dist.Normal(0., 1.)) >>> def model(): ... return numpyro.sample('y', dist.Normal(0., 1.)) >>> # as function decorator (/modifier) >>> y = handlers.seed(model, rng_seed=1)() >>> assert x == y """ def __init__(self, fn=None, rng_seed=None): if isinstance(rng_seed, int) or ( isinstance(rng_seed, (np.ndarray, jnp.ndarray)) and not jnp.shape(rng_seed) ): rng_seed = random.PRNGKey(rng_seed) if not ( isinstance(rng_seed, (np.ndarray, jnp.ndarray)) and rng_seed.dtype == jnp.uint32 and rng_seed.shape == (2,) ): raise TypeError("Incorrect type for rng_seed: {}".format(type(rng_seed))) self.rng_key = rng_seed super(seed, self).__init__(fn) def process_message(self, msg): if ( msg["type"] == "sample" and not msg["is_observed"] and msg["kwargs"]["rng_key"] is None ) or msg["type"] in ["prng_key", "plate", "control_flow"]: if msg["value"] is not None: # no need to create a new key when value is available return self.rng_key, rng_key_sample = random.split(self.rng_key) msg["kwargs"]["rng_key"] = rng_key_sample class substitute(Messenger): """ Given a callable `fn` and a dict `data` keyed by site names (alternatively, a callable `substitute_fn`), return a callable which substitutes all primitive calls in `fn` with values from `data` whose key matches the site name. If the site name is not present in `data`, there is no side effect. If a `substitute_fn` is provided, then the value at the site is replaced by the value returned from the call to `substitute_fn` for the given site. .. note:: This handler is mainly used for internal algorithms. For conditioning a generative model on observed data, please use the :class:`condition` handler. :param fn: Python callable with NumPyro primitives. :param dict data: dictionary of `numpy.ndarray` values keyed by site names. :param substitute_fn: callable that takes in a site dict and returns a numpy array or `None` (in which case the handler has no side effect). **Example:** .. doctest:: >>> from jax import random >>> import numpyro >>> from numpyro.handlers import seed, substitute, trace >>> import numpyro.distributions as dist >>> def model(): ... numpyro.sample('a', dist.Normal(0., 1.)) >>> model = seed(model, random.PRNGKey(0)) >>> exec_trace = trace(substitute(model, {'a': -1})).get_trace() >>> assert exec_trace['a']['value'] == -1 """ def __init__(self, fn=None, data=None, substitute_fn=None): self.substitute_fn = substitute_fn self.data = data if sum((x is not None for x in (data, substitute_fn))) != 1: raise ValueError( "Only one of `data` or `substitute_fn` " "should be provided." ) super(substitute, self).__init__(fn) def process_message(self, msg): if (msg["type"] not in ("sample", "param", "mutable", "plate")) or msg.get( "_control_flow_done", False ): if msg["type"] == "control_flow": if self.data is not None: msg["kwargs"]["substitute_stack"].append(("substitute", self.data)) if self.substitute_fn is not None: msg["kwargs"]["substitute_stack"].append( ("substitute", self.substitute_fn) ) return if self.data is not None: value = self.data.get(msg["name"]) else: value = self.substitute_fn(msg) if value is not None: msg["value"] = value class do(Messenger): """ Given a stochastic function with some sample statements and a dictionary of values at names, set the return values of those sites equal to the values as if they were hard-coded to those values and introduce fresh sample sites with the same names whose values do not propagate. Composes freely with :func:`~numpyro.handlers.condition` to represent counterfactual distributions over potential outcomes. See Single World Intervention Graphs [1] for additional details and theory. This is equivalent to replacing `z = numpyro.sample("z", ...)` with `z = 1.` and introducing a fresh sample site `numpyro.sample("z", ...)` whose value is not used elsewhere. **References:** 1. *Single World Intervention Graphs: A Primer*, Thomas Richardson, James Robins :param fn: a stochastic function (callable containing Pyro primitive calls) :param data: a ``dict`` mapping sample site names to interventions **Example:** .. doctest:: >>> import jax.numpy as jnp >>> import numpyro >>> from numpyro.handlers import do, trace, seed >>> import numpyro.distributions as dist >>> def model(x): ... s = numpyro.sample("s", dist.LogNormal()) ... z = numpyro.sample("z", dist.Normal(x, s)) ... return z ** 2 >>> intervened_model = handlers.do(model, data={"z": 1.}) >>> with trace() as exec_trace: ... z_square = seed(intervened_model, 0)(1) >>> assert exec_trace['z']['value'] != 1. >>> assert not exec_trace['z']['is_observed'] >>> assert not exec_trace['z'].get('stop', None) >>> assert z_square == 1 """ def __init__(self, fn=None, data=None): self.data = data self._intervener_id = str(id(self)) super(do, self).__init__(fn) def process_message(self, msg): if msg["type"] != "sample": return if ( msg.get("_intervener_id", None) != self._intervener_id and self.data.get(msg["name"]) is not None ): if msg.get("_intervener_id", None) is not None: warnings.warn( "Attempting to intervene on variable {} multiple times," "this is almost certainly incorrect behavior".format(msg["name"]), RuntimeWarning, stacklevel=find_stack_level(), ) msg["_intervener_id"] = self._intervener_id # split node, avoid reapplying self recursively to new node new_msg = msg.copy() apply_stack(new_msg) intervention = self.data.get(msg["name"]) msg["name"] = msg["name"] + "__CF" # mangle old name msg["value"] = intervention msg["is_observed"] = True msg["stop"] = True