def wake_generative_with_pyccg(self,
grammar, tasks,
maximumFrontier=None,
enumerationTimeout=None,
CPUs=None,
solver=None,
evaluationTimeout=None):
"""
Dreamcoder wake_generative using PYCCG enumeration to guide exploration.
Enumerates from PyCCG with a timeout and blindly from the EC grammar.
Updates PyCCG using both sets of discovered meanings.
Converts the meanings into EC-style frontiers to be handed off to EC.
"""
# Enumerate PyCCG meanings and update the word learner.
pyccg_meanings = {t : [] for t in tasks}
if self.use_pyccg_enum:
pyccg_meanings = self._update_pyccg_with_distant_batch(tasks, enumerationTimeout)
# Enumerate the remaining tasks using EC-style blind enumeration.
unsolved_tasks = [task for task in tasks if len(pyccg_meanings[task]) == 0]
fallback_frontiers, fallback_times = [], None
if self.use_blind_enum:
fallback_frontiers, fallback_times = multicoreEnumeration(grammar, unsolved_tasks,
maximumFrontier=maximumFrontier,
enumerationTimeout=enumerationTimeout,
CPUs=CPUs,
solver=solver,
evaluationTimeout=evaluationTimeout)
# Log enumeration results.
print("PyCCG model parsing results")
self._describe_pyccg_results(pyccg_meanings)
print("Non-language generative model enumeration results:")
print(Frontier.describe(fallback_frontiers))
# Update PyCCG model with fallback discovered frontiers.
self._update_pyccg_with_supervised_batch(fallback_frontiers) # TODO(catwong, jgauthier): does not yet update.
# Convert and consolidate PyCCG meanings and fallback frontiers for handoff to EC.
pyccg_frontiers = self._pyccg_meanings_to_ec_frontiers(pyccg_meanings)
fallback_frontiers = {frontier.task : frontier for frontier in fallback_frontiers}
all_frontiers = {t : pyccg_frontiers[t] if t in pyccg_frontiers else fallback_frontiers[t] for t in tasks}
all_times = {t : enumerationTimeout if t in pyccg_frontiers else fallback_times[t] for t in tasks}
return list(all_frontiers.values()), all_times
