async def test_use_of_interface():
importer = TrainingDataImporter()
functions_to_test = [
lambda: importer.get_config(),
lambda: importer.get_stories(),
lambda: importer.get_nlu_data(),
lambda: importer.get_domain(),
]
for f in functions_to_test:
with pytest.raises(NotImplementedError):
await f()
async def _train_core_with_validated_data(
file_importer: TrainingDataImporter,
output: Text,
train_path: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
additional_arguments: Optional[Dict] = None,
interpreter: Optional[Interpreter] = None,
) -> Optional[Text]:
"""Train Core with validated training and config data."""
import rasa.core.train
with ExitStack() as stack:
if train_path:
# If the train path was provided, do nothing on exit.
_train_path = train_path
else:
# Otherwise, create a temp train path and clean it up on exit.
_train_path = stack.enter_context(
TempDirectoryPath(tempfile.mkdtemp()))
# normal (not compare) training
print_color("Training Core model...",
color=rasa.shared.utils.io.bcolors.OKBLUE)
domain, config = await asyncio.gather(file_importer.get_domain(),
file_importer.get_config())
await rasa.core.train(
domain_file=domain,
training_resource=file_importer,
output_path=os.path.join(_train_path,
DEFAULT_CORE_SUBDIRECTORY_NAME),
policy_config=config,
additional_arguments=additional_arguments,
interpreter=interpreter,
)
print_color("Core model training completed.",
color=rasa.shared.utils.io.bcolors.OKBLUE)
if train_path is None:
# Only Core was trained.
new_fingerprint = await model.model_fingerprint(file_importer)
return model.package_model(
fingerprint=new_fingerprint,
output_directory=output,
train_path=_train_path,
fixed_model_name=fixed_model_name,
model_prefix="core-",
)
return _train_path
