async def test_events_schema(
monkeypatch: MonkeyPatch, default_agent: Agent, config_path: Text
):
# this allows us to patch the printing part used in debug mode to collect the
# reported events
monkeypatch.setenv("RASA_TELEMETRY_DEBUG", "true")
monkeypatch.setenv("RASA_TELEMETRY_ENABLED", "true")
mock = Mock()
monkeypatch.setattr(telemetry, "print_telemetry_event", mock)
with open(TELEMETRY_EVENTS_JSON) as f:
schemas = json.load(f)["events"]
initial = asyncio.all_tasks()
# Generate all known backend telemetry events, and then use events.json to
# validate their schema.
training_data = TrainingDataImporter.load_from_config(config_path)
with telemetry.track_model_training(training_data, "rasa"):
await asyncio.sleep(1)
telemetry.track_telemetry_disabled()
telemetry.track_data_split(0.5, "nlu")
telemetry.track_validate_files(True)
telemetry.track_data_convert("yaml", "nlu")
telemetry.track_tracker_export(5, TrackerStore(domain=None), EventBroker())
telemetry.track_interactive_learning_start(True, False)
telemetry.track_server_start([CmdlineInput()], None, None, 42, True)
telemetry.track_project_init("tests/")
telemetry.track_shell_started("nlu")
telemetry.track_rasa_x_local()
telemetry.track_visualization()
telemetry.track_core_model_test(5, True, default_agent)
telemetry.track_nlu_model_test(TrainingData())
pending = asyncio.all_tasks() - initial
await asyncio.gather(*pending)
assert mock.call_count == 15
for args, _ in mock.call_args_list:
event = args[0]
# `metrics_id` automatically gets added to all event but is
# not part of the schema so we need to remove it before validation
del event["properties"]["metrics_id"]
jsonschema.validate(
instance=event["properties"], schema=schemas[event["event"]]
)
def split_nlu_data(args: argparse.Namespace) -> None:
"""Load data from a file path and split the NLU data into test and train examples.
Args:
args: Commandline arguments
"""
data_path = rasa.cli.utils.get_validated_path(args.nlu, "nlu", DEFAULT_DATA_PATH)
data_path = rasa.shared.data.get_nlu_directory(data_path)
nlu_data = rasa.shared.nlu.training_data.loading.load_data(data_path)
extension = rasa.shared.nlu.training_data.util.get_file_format_extension(data_path)
train, test = nlu_data.train_test_split(args.training_fraction, args.random_seed)
train.persist(args.out, filename=f"training_data{extension}")
test.persist(args.out, filename=f"test_data{extension}")
telemetry.track_data_split(args.training_fraction, "nlu")
