def _test_core(stories: Optional[Text], agent: "Agent", output_directory: Text,
**kwargs: Any) -> None:
from rasa.core.test import test
loop = asyncio.get_event_loop()
loop.run_until_complete(
test(stories, agent, out_directory=output_directory, **kwargs))
def test_core(
model: Optional[Text] = None,
stories: Optional[Text] = None,
output: Text = DEFAULT_RESULTS_PATH,
additional_arguments: Optional[Dict] = None,
) -> None:
"""Tests a trained Core model against a set of test stories."""
import rasa.model
from rasa.shared.nlu.interpreter import RegexInterpreter
from rasa.core.agent import Agent
if additional_arguments is None:
additional_arguments = {}
if output:
rasa.shared.utils.io.create_directory(output)
try:
unpacked_model = rasa.model.get_model(model)
except ModelNotFound:
rasa.shared.utils.cli.print_error(
"Unable to test: could not find a model. Use 'rasa train' to train a "
"Rasa model and provide it via the '--model' argument.")
return
_agent = Agent.load(unpacked_model)
if _agent.policy_ensemble is None:
rasa.shared.utils.cli.print_error(
"Unable to test: could not find a Core model. Use 'rasa train' to train a "
"Rasa model and provide it via the '--model' argument.")
if isinstance(_agent.interpreter, RegexInterpreter):
rasa.shared.utils.cli.print_warning(
"No NLU model found. Using default 'RegexInterpreter' for end-to-end "
"evaluation. If you added actual user messages to your test stories "
"this will likely lead to the tests failing. In that case, you need "
"to train a NLU model first, e.g. using `rasa train`.")
from rasa.core.test import test
kwargs = rasa.shared.utils.common.minimal_kwargs(additional_arguments,
test,
["stories", "agent"])
rasa.utils.common.run_in_loop(
test(stories, _agent, out_directory=output, **kwargs))