def compile(cls, source_net, compiled_net):
"""Add observed nodes to the computation graph.
Parameters
----------
source_net : nx.DiGraph
compiled_net : nx.DiGraph
Returns
-------
compiled_net : nx.Digraph
"""
logger.debug("{} compiling...".format(cls.__name__))
observable = []
uses_observed = []
for node in nx.topological_sort(source_net):
state = source_net.nodes[node]['attr_dict']
if state.get('_observable'):
observable.append(node)
cls.make_observed_copy(node, compiled_net)
elif state.get('_uses_observed'):
uses_observed.append(node)
obs_node = cls.make_observed_copy(node, compiled_net,
args_to_tuple)
# Make edge to the using node
compiled_net.add_edge(obs_node, node, param='observed')
else:
continue
# Copy the edges
if not state.get('_stochastic'):
obs_node = observed_name(node)
for parent in source_net.predecessors(node):
if parent in observable:
link_parent = observed_name(parent)
else:
link_parent = parent
compiled_net.add_edge(link_parent, obs_node,
**source_net[parent][node].copy())
# Check that there are no stochastic nodes in the ancestors
for node in uses_observed:
# Use the observed version to query observed ancestors in the compiled_net
obs_node = observed_name(node)
for ancestor_node in nx.ancestors(compiled_net, obs_node):
if '_stochastic' in source_net.nodes.get(ancestor_node, {}):
raise ValueError(
"Observed nodes must be deterministic. Observed "
"data depends on a non-deterministic node {}.".format(
ancestor_node))
return compiled_net
def load(cls, context, compiled_net, batch_index):
"""Add the observed data to the `compiled_net`.
Parameters
----------
context : ComputationContext
compiled_net : nx.DiGraph
batch_index : int
Returns
-------
net : nx.DiGraph
Loaded net, which is the `compiled_net` that has been loaded with data that
can depend on the batch_index.
"""
observed = compiled_net.graph['observed']
for name, obs in observed.items():
obs_name = observed_name(name)
if not compiled_net.has_node(obs_name):
continue
compiled_net.node[obs_name] = dict(output=obs)
del compiled_net.graph['observed']
return compiled_net
def make_observed_copy(cls, node, compiled_net, operation=None):
"""Make a renamed copy of an observed node and add it to `compiled_net`.
Parameters
----------
node : str
compiled_net : nx.DiGraph
operation : callable, optional
Returns
-------
str
"""
obs_node = observed_name(node)
if compiled_net.has_node(obs_node):
raise ValueError(
"Observed node {} already exists!".format(obs_node))
if operation is None:
compiled_dict = compiled_net.nodes[node].copy()
else:
compiled_dict = dict(operation=operation)
compiled_net.add_node(obs_node, **compiled_dict)
return obs_node
def load(cls, context, compiled_net, batch_index):
for name, obs in context.observed.items():
obs_name = observed_name(name)
if not compiled_net.has_node(obs_name):
continue
compiled_net.node[obs_name] = dict(output=obs)
return compiled_net
def make_observed_copy(cls, node, compiled_net, operation=None):
obs_node = observed_name(node)
if compiled_net.has_node(obs_node):
raise ValueError(
"Observed node {} already exists!".format(obs_node))
if operation is None:
compiled_dict = compiled_net.node[node].copy()
else:
compiled_dict = dict(operation=operation)
compiled_net.add_node(obs_node, compiled_dict)
return obs_node
def observed(self):
obs_name = observed_name(self.name)
result = self.model.generate(0, obs_name)
return result[obs_name]