def _fallback_model(self):
meta = Metadata(
{
"pipeline": [{
"name":
"KeywordIntentClassifier",
"class":
utils.module_path_from_object(KeywordIntentClassifier())
}]
}, "")
return Interpreter.create(meta, self._component_builder)
def persist(self,
path: Text,
persistor: Optional[Persistor] = None,
project_name: Text = None,
fixed_model_name: Text = None) -> Text:
"""Persist all components of the pipeline to the passed path.
Returns the directory of the persisted model."""
timestamp = datetime.datetime.now().strftime('%Y%m%d-%H%M%S')
metadata = {
"language": self.config["language"],
"adapter": self.config['adapter'],
"pipeline": [],
}
if project_name is None:
project_name = "default"
if fixed_model_name:
model_name = fixed_model_name
else:
model_name = "model_" + timestamp
path = make_path_absolute(path)
dir_name = os.path.join(path, project_name, model_name)
create_dir(dir_name)
if self.training_data:
metadata.update(self.training_data.persist(dir_name))
for i, component in enumerate(self.pipeline):
file_name = self._file_name(i, component.name)
update = component.persist(file_name, dir_name)
component_meta = component.component_config
if update:
component_meta.update(update)
component_meta["class"] = utils.module_path_from_object(component)
metadata["pipeline"].append(component_meta)
Metadata(metadata, dir_name).persist(dir_name)
if persistor is not None:
persistor.persist(dir_name, model_name, project_name)
logger.info("Successfully saved model into "
"'{}'".format(os.path.abspath(dir_name)))
return dir_name
def fallback_model(component_builder: ComponentBuilder):
meta = Metadata(
{
"pipeline": [
{
"name": "KeywordIntentClassifier",
"class": utils.module_path_from_object(
KeywordIntentClassifier()
),
}
]
},
"",
)
interpreter = Interpreter.create(meta, component_builder)
return NLUModel(FALLBACK_MODEL_NAME, interpreter)
def persist(
self,
path: Text,
persistor: Optional[Persistor] = None,
fixed_model_name: Text = None,
persist_nlu_training_data: bool = False,
) -> Text:
"""Persist all components of the pipeline to the passed path.
Returns the directory of the persisted model."""
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
metadata = {"language": self.config["language"], "pipeline": []}
if fixed_model_name:
model_name = fixed_model_name
else:
model_name = NLU_MODEL_NAME_PREFIX + timestamp
path = os.path.abspath(path)
dir_name = os.path.join(path, model_name)
rasa.shared.utils.io.create_directory(dir_name)
if self.training_data and persist_nlu_training_data:
metadata.update(self.training_data.persist(dir_name))
for i, component in enumerate(self.pipeline):
file_name = self._file_name(i, component.name)
update = component.persist(file_name, dir_name)
component_meta = component.component_config
if update:
component_meta.update(update)
component_meta["class"] = utils.module_path_from_object(component)
metadata["pipeline"].append(component_meta)
Metadata(metadata, dir_name).persist(dir_name)
if persistor is not None:
persistor.persist(dir_name, model_name)
logger.info(
"Successfully saved model into '{}'".format(os.path.abspath(dir_name))
)
return dir_name
