def persist(self, path):
"""Persist the NLU engine at the given directory path
Args:
path (str): the location at which the nlu engine must be persisted.
This path must not exist when calling this function.
"""
directory_path = Path(path)
directory_path.mkdir()
parsers_count = defaultdict(int)
intent_parsers = []
for parser in self.intent_parsers:
parser_name = parser.unit_name
parsers_count[parser_name] += 1
count = parsers_count[parser_name]
if count > 1:
parser_name = "{n}_{c}".format(n=parser_name, c=count)
parser_path = directory_path / parser_name
parser.persist(parser_path)
intent_parsers.append(parser_name)
config = None
if self.config is not None:
config = self.config.to_dict()
model = {
"unit_name": self.unit_name,
"dataset_metadata": self._dataset_metadata,
"intent_parsers": intent_parsers,
"config": config,
"model_version": __model_version__,
"training_package_version": __version__
}
model_json = json_string(model)
model_path = directory_path / "nlu_engine.json"
with model_path.open(mode="w") as f:
f.write(model_json)
if self.fitted:
required_resources = self.config.get_required_resources()
if required_resources:
language = self._dataset_metadata["language_code"]
resources_path = directory_path / "resources"
resources_path.mkdir()
persist_resources(resources_path / language,
required_resources, language)
def persist(self, path):
"""Persists the NLU engine at the given directory path
Args:
path (str or pathlib.Path): the location at which the nlu engine
must be persisted. This path must not exist when calling this
function.
Raises:
PersistingError: when persisting to a path which already exists
"""
path.mkdir()
parsers_count = defaultdict(int)
intent_parsers = []
for parser in self.intent_parsers:
parser_name = parser.unit_name
parsers_count[parser_name] += 1
count = parsers_count[parser_name]
if count > 1:
parser_name = "{n}_{c}".format(n=parser_name, c=count)
parser_path = path / parser_name
parser.persist(parser_path)
intent_parsers.append(parser_name)
config = None
if self.config is not None:
config = self.config.to_dict()
builtin_entity_parser = None
if self.builtin_entity_parser is not None:
builtin_entity_parser = "builtin_entity_parser"
builtin_entity_parser_path = path / builtin_entity_parser
self.builtin_entity_parser.persist(builtin_entity_parser_path)
custom_entity_parser = None
if self.custom_entity_parser is not None:
custom_entity_parser = "custom_entity_parser"
custom_entity_parser_path = path / custom_entity_parser
self.custom_entity_parser.persist(custom_entity_parser_path)
model = {
"unit_name": self.unit_name,
"dataset_metadata": self.dataset_metadata,
"intent_parsers": intent_parsers,
"custom_entity_parser": custom_entity_parser,
"builtin_entity_parser": builtin_entity_parser,
"config": config,
"model_version": __model_version__,
"training_package_version": __version__
}
model_json = json_string(model)
model_path = path / "nlu_engine.json"
with model_path.open(mode="w") as f:
f.write(model_json)
if self.fitted:
required_resources = self.config.get_required_resources()
language = self.dataset_metadata["language_code"]
resources_path = path / "resources"
resources_path.mkdir()
persist_resources(self.resources, resources_path / language,
required_resources)