def run_core_test(args: argparse.Namespace) -> None:
"""Run core tests."""
from rasa.test import test_core_models_in_directory, test_core, test_core_models
stories = cli_utils.get_validated_path(args.stories, "stories",
DEFAULT_DATA_PATH)
stories = rasa.shared.data.get_test_directory(stories)
output = args.out or DEFAULT_RESULTS_PATH
args.errors = not args.no_errors
rasa.shared.utils.io.create_directory(output)
if isinstance(args.model, list) and len(args.model) == 1:
args.model = args.model[0]
if isinstance(args.model, str):
model_path = cli_utils.get_validated_path(args.model, "model",
DEFAULT_MODELS_PATH)
if args.evaluate_model_directory:
test_core_models_in_directory(args.model, stories, output)
else:
test_core(
model=model_path,
stories=stories,
output=output,
additional_arguments=vars(args),
)
else:
test_core_models(args.model, stories, output)
def test_core(args: argparse.Namespace) -> None:
from rasa.test import test_core
endpoints = get_validated_path(
args.endpoints, "endpoints", DEFAULT_ENDPOINTS_PATH, True
)
stories = get_validated_path(args.stories, "stories", DEFAULT_DATA_PATH)
stories = data.get_core_directory(stories)
output = args.out or DEFAULT_RESULTS_PATH
if not os.path.exists(output):
os.makedirs(output)
if isinstance(args.model, list) and len(args.model) == 1:
args.model = args.model[0]
if isinstance(args.model, str):
model_path = get_validated_path(args.model, "model", DEFAULT_MODELS_PATH)
test_core(
model=model_path,
stories=stories,
endpoints=endpoints,
output=output,
kwargs=vars(args),
)
else:
test_compare_core(args.model, stories, output)
def run_core_test(args: argparse.Namespace) -> None:
"""Run core tests."""
from rasa import data
from rasa.test import test_core_models_in_directory, test_core, test_core_models
endpoints = cli_utils.get_validated_path(args.endpoints, "endpoints",
DEFAULT_ENDPOINTS_PATH, True)
stories = cli_utils.get_validated_path(args.stories, "stories",
DEFAULT_DATA_PATH)
stories = data.get_core_directory(stories)
output = args.out or DEFAULT_RESULTS_PATH
io_utils.create_directory(output)
if isinstance(args.model, list) and len(args.model) == 1:
args.model = args.model[0]
if isinstance(args.model, str):
model_path = cli_utils.get_validated_path(args.model, "model",
DEFAULT_MODELS_PATH)
if args.evaluate_model_directory:
test_core_models_in_directory(args.model, stories, output)
else:
test_core(
model=model_path,
stories=stories,
endpoints=endpoints,
output=output,
additional_arguments=vars(args),
)
else:
test_core_models(args.model, stories, output)
def test_core(args: argparse.Namespace) -> None:
from rasa.test import test_core
endpoints = get_validated_path(
args.endpoints, "endpoints", DEFAULT_ENDPOINTS_PATH, True
)
stories = get_validated_path(args.stories, "stories", DEFAULT_DATA_PATH)
stories = data.get_core_directory(stories)
output = args.output or DEFAULT_RESULTS_PATH
args.config = get_validated_path(args.config, "config", DEFAULT_CONFIG_PATH)
if len(args.model) == 1:
args.model = args.model[0]
model_path = get_validated_path(args.model, "model", DEFAULT_MODELS_PATH)
test_core(
model=model_path,
stories=stories,
endpoints=endpoints,
output=output,
kwargs=vars(args),
)
else:
test_compare(args.model, stories, output)
def test_e2e_warning_if_no_nlu_model(monkeypatch: MonkeyPatch,
trained_core_model: Text,
capsys: CaptureFixture):
from rasa.test import test_core
# Patching is bit more complicated as we have a module `train` and function
# with the same name 😬
monkeypatch.setattr(sys.modules["rasa.core.test"], "test",
asyncio.coroutine(lambda *_, **__: True))
test_core(trained_core_model, additional_arguments={"e2e": True})
assert "No NLU model found. Using default" in capsys.readouterr().out
def test_core(args: argparse.Namespace, model_path: Optional[Text] = None
) -> None:
from rasa.test import test_core
args.model = get_validated_path(args.model, "model", DEFAULT_MODELS_PATH)
args.endpoints = get_validated_path(args.endpoints, "endpoints",
DEFAULT_ENDPOINTS_PATH, True)
args.config = get_validated_path(args.config, "config", DEFAULT_CONFIG_PATH)
args.stories = get_validated_path(args.stories, "stories",
DEFAULT_DATA_PATH)
args.stories = data.get_core_directory(args.stories)
test_core(model_path=model_path, **vars(args))
def run_core_test(args: argparse.Namespace) -> None:
"""Run core tests."""
from rasa.test import test_core_models_in_directory, test_core, test_core_models
stories = rasa.cli.utils.get_validated_path(args.stories, "stories",
DEFAULT_DATA_PATH)
if args.e2e:
stories = rasa.shared.data.get_test_directory(stories)
else:
stories = rasa.shared.data.get_core_directory(stories)
output = args.out or DEFAULT_RESULTS_PATH
args.errors = not args.no_errors
rasa.shared.utils.io.create_directory(output)
if isinstance(args.model, list) and len(args.model) == 1:
args.model = args.model[0]
if args.model is None:
rasa.shared.utils.cli.print_error(
"No model provided. Please make sure to specify the model to test with '--model'."
)
return
if isinstance(args.model, str):
model_path = rasa.cli.utils.get_validated_path(args.model, "model",
DEFAULT_MODELS_PATH)
if args.evaluate_model_directory:
test_core_models_in_directory(args.model, stories, output)
else:
test_core(
model=model_path,
stories=stories,
output=output,
additional_arguments=vars(args),
)
else:
test_core_models(args.model, stories, output)
rasa.shared.utils.cli.print_info(
f"Failed stories written to '{os.path.join(output, FAILED_STORIES_FILE)}'"
)
async def test_interpreter_passed_to_agent(
monkeypatch: MonkeyPatch, trained_rasa_model: Text
):
from rasa.test import test_core
# Patching is bit more complicated as we have a module `train` and function
# with the same name 😬
monkeypatch.setattr(
sys.modules["rasa.test"], "_test_core", asyncio.coroutine(lambda *_, **__: True)
)
agent_load = Mock()
monkeypatch.setattr(Agent, "load", agent_load)
test_core(trained_rasa_model)
agent_load.assert_called_once()
_, _, kwargs = agent_load.mock_calls[0]
assert isinstance(kwargs["interpreter"], RasaNLUInterpreter)
async def test_e2e_warning_if_no_nlu_model(
monkeypatch: MonkeyPatch, trained_core_model: Text, capsys: CaptureFixture
):
from rasa.test import test_core
# Patching is bit more complicated as we have a module `train` and function
# with the same name 😬
monkeypatch.setattr(
sys.modules["rasa.test"], "_test_core", asyncio.coroutine(lambda *_, **__: True)
)
agent_load = Mock()
monkeypatch.setattr(Agent, "load", agent_load)
test_core(trained_core_model, additional_arguments={"e2e": True})
assert "No NLU model found. Using default" in capsys.readouterr().out
agent_load.assert_called_once()
_, _, kwargs = agent_load.mock_calls[0]
assert isinstance(kwargs["interpreter"], RegexInterpreter)
def test_core(args: argparse.Namespace,
model_path: Optional[Text] = None) -> None:
from rasa.test import test_core
model = get_validated_path(args.model, "model", DEFAULT_MODELS_PATH)
endpoints = get_validated_path(args.endpoints, "endpoints",
DEFAULT_ENDPOINTS_PATH, True)
stories = get_validated_path(args.stories, "stories", DEFAULT_DATA_PATH)
stories = data.get_core_directory(stories)
output = args.output or DEFAULT_RESULTS_PATH
args.config = get_validated_path(args.config, "config",
DEFAULT_CONFIG_PATH)
test_core(
model=model,
stories=stories,
endpoints=endpoints,
model_path=model_path,
output=output,
kwargs=vars(args),
)
def test(args: argparse.Namespace):
test_core(args)
test_nlu(args)
def test(args: argparse.Namespace):
model_path = get_validated_path(args.model, "model", DEFAULT_MODELS_PATH)
unpacked_model = get_model(model_path)
test_core(args, unpacked_model)
test_nlu(args, unpacked_model)
