def _persist_metadata(self, path, dump_flattened_stories=False):
# type: (Text) -> None
"""Persists the domain specification to storage."""
# make sure the directory we persist to exists
domain_spec_path = os.path.join(path, 'policy_metadata.json')
training_data_path = os.path.join(path, 'stories.md')
utils.create_dir_for_file(domain_spec_path)
policy_names = [utils.module_path_from_instance(p)
for p in self.policies]
training_events = self._training_events_from_trackers(
self.training_trackers)
action_fingerprints = self._create_action_fingerprints(training_events)
metadata = {
"action_fingerprints": action_fingerprints,
"rasa_core": rasa_core.__version__,
"max_histories": self._max_histories(),
"ensemble_name": self.__module__ + "." + self.__class__.__name__,
"policy_names": policy_names
}
utils.dump_obj_as_json_to_file(domain_spec_path, metadata)
# if there are lots of stories, saving flattened stories takes a long
# time, so this is turned off by default
if dump_flattened_stories:
training.persist_data(self.training_trackers, training_data_path)
def _persist_metadata(self,
path: Text,
dump_flattened_stories: bool = False) -> None:
"""Persists the domain specification to storage."""
# make sure the directory we persist exists
domain_spec_path = os.path.join(path, 'metadata.json')
training_data_path = os.path.join(path, 'stories.md')
utils.create_dir_for_file(domain_spec_path)
policy_names = [utils.module_path_from_instance(p)
for p in self.policies]
training_events = self._training_events_from_trackers(
self.training_trackers)
action_fingerprints = self._create_action_fingerprints(training_events)
metadata = {
"action_fingerprints": action_fingerprints,
"python": ".".join([str(s) for s in sys.version_info[:3]]),
"max_histories": self._max_histories(),
"ensemble_name": self.__module__ + "." + self.__class__.__name__,
"policy_names": policy_names,
"trained_at": self.date_trained
}
self._add_package_version_info(metadata)
utils.dump_obj_as_json_to_file(domain_spec_path, metadata)
# if there are lots of stories, saving flattened stories takes a long
# time, so this is turned off by default
if dump_flattened_stories:
training.persist_data(self.training_trackers, training_data_path)
def _persist_metadata(self, path, dump_flattened_stories=False):
# type: (Text) -> None
"""Persists the domain specification to storage."""
# make sure the directory we persist to exists
domain_spec_path = os.path.join(path, 'policy_metadata.json')
training_data_path = os.path.join(path, 'stories.md')
utils.create_dir_for_file(domain_spec_path)
policy_names = [
utils.module_path_from_instance(p) for p in self.policies
]
training_events = self._training_events_from_trackers(
self.training_trackers)
action_fingerprints = self._create_action_fingerprints(training_events)
metadata = {
"action_fingerprints": action_fingerprints,
"rasa_core": rasa_core.__version__,
"max_histories": self._max_histories(),
"ensemble_name": self.__module__ + "." + self.__class__.__name__,
"policy_names": policy_names
}
utils.dump_obj_as_json_to_file(domain_spec_path, metadata)
# if there are lots of stories, saving flattened stories takes a long
# time, so this is turned off by default
if dump_flattened_stories:
training.persist_data(self.training_trackers, training_data_path)
def persist(self, path: Text) -> None:
if self.model:
self.featurizer.persist(path)
meta = {
"priority": self.priority,
"model": "keras_model.h5",
"epochs": self.current_epoch
}
meta_file = os.path.join(path, 'keras_policy.json')
utils.dump_obj_as_json_to_file(meta_file, meta)
model_file = os.path.join(path, meta['model'])
# makes sure the model directory exists
utils.create_dir_for_file(model_file)
with self.graph.as_default(), self.session.as_default():
self.model.save(model_file, overwrite=True)
tf_config_file = os.path.join(path, "keras_policy.tf_config.pkl")
with open(tf_config_file, 'wb') as f:
pickle.dump(self._tf_config, f)
else:
warnings.warn("Persist called without a trained model present. "
"Nothing to persist then!")
def persist_specification(self, model_path):
# type: (Text, List[Text]) -> None
"""Persists the domain specification to storage."""
domain_spec_path = os.path.join(model_path, 'domain.json')
utils.create_dir_for_file(domain_spec_path)
metadata = {"features": self.input_features}
utils.dump_obj_as_json_to_file(domain_spec_path, metadata)
def persist(self, path: Text) -> None:
self.featurizer.persist(path)
memorized_file = os.path.join(path, 'memorized_turns.json')
data = {"max_history": self.max_history, "lookup": self.lookup}
utils.create_dir_for_file(memorized_file)
utils.dump_obj_as_json_to_file(memorized_file, data)
def persist_specification(self, model_path: Text) -> None:
"""Persists the domain specification to storage."""
domain_spec_path = os.path.join(model_path, 'domain.json')
utils.create_dir_for_file(domain_spec_path)
metadata = {"states": self.input_states}
utils.dump_obj_as_json_to_file(domain_spec_path, metadata)
def persist_specification(self, model_path):
# type: (Text, List[Text]) -> None
"""Persists the domain specification to storage."""
domain_spec_path = os.path.join(model_path, 'domain.json')
utils.create_dir_for_file(domain_spec_path)
metadata = {"features": self.input_features}
with io.open(domain_spec_path, 'w') as f:
f.write(str(json.dumps(metadata, indent=2)))
def persist(self, path: Text) -> None:
"""Persists the policy to storage."""
config_file = os.path.join(path, 'fallback_policy.json')
meta = {
"nlu_threshold": self.nlu_threshold,
"core_threshold": self.core_threshold,
"fallback_action_name": self.fallback_action_name
}
utils.create_dir_for_file(config_file)
utils.dump_obj_as_json_to_file(config_file, meta)
def persist(self, path):
# type: (Text) -> None
"""Persists the policy to storage."""
config_file = os.path.join(path, 'confirmation_policy.json')
meta = {
"nlu_threshold": self.nlu_threshold,
"confirmation_action_name": self.confirmation_action_name
}
utils.create_dir_for_file(config_file)
utils.dump_obj_as_json_to_file(config_file, meta)
def persist(self, path):
# type: (Text) -> None
"""Persists the policy to storage."""
config_file = os.path.join(path, 'fallback_policy.json')
meta = {
"nlu_threshold": self.nlu_threshold,
"core_threshold": self.core_threshold,
"fallback_action_name": self.fallback_action_name
}
utils.create_dir_for_file(config_file)
utils.dump_obj_as_json_to_file(config_file, meta)
def persist_specification(self, model_path):
# type: (Text) -> None
"""Persists the domain specification to storage."""
domain_spec_path = os.path.join(model_path, 'domain.json')
utils.create_dir_for_file(domain_spec_path)
metadata = {
"states": self.input_states
}
utils.dump_obj_as_json_to_file(domain_spec_path, metadata)
def persist(self, path: Text) -> None:
"""Persists the policy to storage."""
config_file = os.path.join(path, "bottis_policy.json")
meta = {
"priority": self.priority,
"nlu_threshold": self.nlu_threshold,
"core_threshold": self.core_threshold,
"custom_response_action_name": self.custom_response_action_name,
}
utils.create_dir_for_file(config_file)
utils.dump_obj_as_json_to_file(config_file, meta)
def persist(self, path):
# type: (Text) -> None
self.featurizer.persist(path)
memorized_file = os.path.join(path, 'memorized_turns.json')
data = {
"max_history": self.max_history,
"lookup": self.lookup
}
utils.create_dir_for_file(memorized_file)
utils.dump_obj_as_json_to_file(memorized_file, data)
def persist(self, path: Text) -> None:
"""Persists the policy to storage."""
config_file = os.path.join(path, 'two_stage_fallback_policy.json')
meta = {
"priority": self.priority,
"nlu_threshold": self.nlu_threshold,
"core_threshold": self.core_threshold,
"fallback_core_action_name": self.fallback_action_name,
"fallback_nlu_action_name": self.fallback_nlu_action_name,
"deny_suggestion_intent_name": self.deny_suggestion_intent_name,
}
utils.create_dir_for_file(config_file)
utils.dump_obj_as_json_to_file(config_file, meta)
def persist(self, path):
# tupe: (Text) -> None
if self.model:
self.featurizer.persist(path)
meta = {"model": "torch_model.h5", "epochs": self.current_epoch}
config_file = os.path.join(path, "torch_policy.json")
utils.dump_obj_as_json_to_file(config_file, meta)
model_file = os.path.join(path, meta["model"])
utils.create_dir_for_file(model_file)
torch.save(self.model, model_file)
else:
warnings.warn("Persist called without a trained model present. "
"Nothing to persist.")
def persist(self, path):
if self.model:
arch_file = os.path.join(path, 'keras_arch.json')
weights_file = os.path.join(path, 'keras_weights.h5')
config_file = os.path.join(path, 'keras_policy.json')
# makes sure the model directory exists
utils.create_dir_for_file(weights_file)
utils.dump_obj_as_str_to_file(arch_file, self.model.to_json())
self._persist_configuration(config_file)
self.model.save_weights(weights_file, overwrite=True)
else:
warnings.warn("Persist called without a trained model present. "
"Nothing to persist then!")
def persist(self, path):
if self.model:
arch_file = os.path.join(path, 'keras_arch.json')
weights_file = os.path.join(path, 'keras_weights.h5')
utils.create_dir_for_file(weights_file)
with io.open(arch_file, 'w') as f:
f.write(str(self.model.to_json()))
with io.open(os.path.join(path, 'keras_policy.json'), 'w') as f:
f.write(str(json.dumps({
"arch": "keras_arch.json",
"weights": "keras_weights.h5",
"epochs": self.current_epoch})))
self.model.save_weights(weights_file, overwrite=True)
else:
warnings.warn("Persist called without a trained model present. "
"Nothing to persist then!")
def _persist_metadata(self, path, max_history):
# type: (Text, Optional[int]) -> None
"""Persists the domain specification to storage."""
domain_spec_path = os.path.join(path, 'policy_metadata.json')
utils.create_dir_for_file(domain_spec_path)
policy_names = [
p.__module__ + "." + p.__class__.__name__ for p in self.policies
]
metadata = {
"rasa_core": rasa_core.__version__,
"max_history": max_history,
"ensemble_name": self.__module__ + "." + self.__class__.__name__,
"policy_names": policy_names
}
with io.open(domain_spec_path, 'w') as f:
f.write(str(json.dumps(metadata, indent=2)))
def persist(self, path):
# type: (Text) -> None
if self.model:
self.featurizer.persist(path)
arch_file = os.path.join(path, 'keras_arch.json')
weights_file = os.path.join(path, 'keras_weights.h5')
config_file = os.path.join(path, 'keras_policy.json')
# makes sure the model directory exists
utils.create_dir_for_file(weights_file)
utils.dump_obj_as_str_to_file(arch_file, self.model.to_json())
self._persist_configuration(config_file)
self.model.save_weights(weights_file, overwrite=True)
else:
warnings.warn("Persist called without a trained model present. "
"Nothing to persist then!")
def persist(self, path):
# type: (Text) -> None
if self.model:
self.featurizer.persist(path)
meta = {"model": "keras_model.h5", "epochs": self.current_epoch}
config_file = os.path.join(path, 'keras_policy.json')
utils.dump_obj_as_json_to_file(config_file, meta)
model_file = os.path.join(path, meta['model'])
# makes sure the model directory exists
utils.create_dir_for_file(model_file)
with self.graph.as_default(), self.session.as_default():
self.model.save(model_file, overwrite=True)
else:
warnings.warn("Persist called without a trained model present. "
"Nothing to persist then!")
def persist(self, path):
# type: (Text) -> None
if self.model:
self.featurizer.persist(path)
meta = {"model": "keras_model.h5",
"epochs": self.current_epoch}
config_file = os.path.join(path, 'keras_policy.json')
utils.dump_obj_as_json_to_file(config_file, meta)
model_file = os.path.join(path, meta['model'])
# makes sure the model directory exists
utils.create_dir_for_file(model_file)
with self.graph.as_default(), self.session.as_default():
self.model.save(model_file, overwrite=True)
else:
warnings.warn("Persist called without a trained model present. "
"Nothing to persist then!")
def _persist_metadata(self, path, max_history):
# type: (Text, Optional[int]) -> None
"""Persists the domain specification to storage."""
# make sure the directory we persist to exists
domain_spec_path = os.path.join(path, 'policy_metadata.json')
utils.create_dir_for_file(domain_spec_path)
policy_names = [utils.module_path_from_instance(p)
for p in self.policies]
training_events = self.training_metadata.get("events", {})
action_fingerprints = self._create_action_fingerprints(training_events)
metadata = {
"action_fingerprints": action_fingerprints,
"rasa_core": rasa_core.__version__,
"max_history": max_history,
"ensemble_name": self.__module__ + "." + self.__class__.__name__,
"policy_names": policy_names
}
utils.dump_obj_as_json_to_file(domain_spec_path, metadata)
def persist(self, path):
featurizer_file = os.path.join(path, "featurizer.json")
utils.create_dir_for_file(featurizer_file)
with io.open(featurizer_file, 'w', encoding="utf-8") as f:
# noinspection PyTypeChecker
f.write(str(jsonpickle.encode(self)))
def persist(self, path):
memorized_file = os.path.join(path, 'memorized_turns.json')
data = {"lookup": self.lookup}
utils.create_dir_for_file(memorized_file)
utils.dump_obj_as_json_to_file(memorized_file, data)
def persist(self, path):
featurizer_file = os.path.join(path, "featurizer.json")
utils.create_dir_for_file(featurizer_file)
with io.open(featurizer_file, 'w') as f:
f.write(str(jsonpickle.encode(self)))
def persist(self, path):
memorized_file = os.path.join(path, 'memorized_turns.json')
data = {"lookup": self.lookup}
utils.create_dir_for_file(memorized_file)
with io.open(memorized_file, 'w') as f:
f.write(str(json.dumps(data, indent=2)))
def persist(self, path):
featurizer_file = os.path.join(path, "featurizer.json")
utils.create_dir_for_file(featurizer_file)
with io.open(featurizer_file, 'w') as f:
f.write(str(jsonpickle.encode(self)))
