def interactive(args: argparse.Namespace) -> None:
_set_not_required_args(args)
file_importer = TrainingDataImporter.load_from_config(
args.config, args.domain, args.data)
if args.model is None:
loop = asyncio.get_event_loop()
story_graph = loop.run_until_complete(file_importer.get_stories())
if not story_graph or story_graph.is_empty():
rasa.shared.utils.cli.print_error_and_exit(
"Could not run interactive learning without either core data or a model containing core data."
)
zipped_model = train.train_core(
args) if args.core_only else train.train(args)
if not zipped_model:
rasa.shared.utils.cli.print_error_and_exit(
"Could not train an initial model. Either pass paths "
"to the relevant training files (`--data`, `--config`, `--domain`), "
"or use 'rasa train' to train a model.")
else:
zipped_model = get_provided_model(args.model)
if not (zipped_model and os.path.exists(zipped_model)):
rasa.shared.utils.cli.print_error_and_exit(
f"Interactive learning process cannot be started as no initial model was "
f"found at path '{args.model}'. Use 'rasa train' to train a model."
)
if not args.skip_visualization:
logger.info(f"Loading visualization data from {args.data}.")
perform_interactive_learning(args, zipped_model, file_importer)
def interactive(args: argparse.Namespace):
_set_not_required_args(args)
if args.model is None:
check_training_data(args)
zipped_model = train.train(args)
else:
zipped_model = get_provided_model(args.model)
perform_interactive_learning(args, zipped_model)
def interactive(args: argparse.Namespace):
args.fixed_model_name = None
args.store_uncompressed = False
if args.model is None:
check_training_data(args)
zipped_model = train.train(args)
else:
zipped_model = get_provided_model(args.model)
perform_interactive_learning(args, zipped_model)
def test_pass_arguments_to_rasa_train(default_stack_config: Text,
monkeypatch: MonkeyPatch) -> None:
# Create parser
parser = argparse.ArgumentParser()
sub_parser = parser.add_subparsers()
interactive.add_subparser(sub_parser, [])
# Parse interactive command
args = parser.parse_args(["interactive", "--config", default_stack_config])
interactive._set_not_required_args(args)
# Mock actual training
mock = Mock(return_value=TrainingResult(code=0))
monkeypatch.setattr(rasa, "train", mock.method)
# If the `Namespace` object does not have all required fields this will throw
train.train(args)
# Assert `train` was actually called
mock.method.assert_called_once()
def interactive(args: argparse.Namespace):
from rasa.core.train import do_interactive_learning
args.finetune = False # Don't support finetuning
zipped_model = train.train(args)
model_path = model.unpack_model(zipped_model)
args.core, args.nlu = model.get_model_subdirectories(model_path)
stories_directory = data.get_core_directory(args.data)
do_interactive_learning(args, stories_directory)
shutil.rmtree(model_path)
def interactive(args: argparse.Namespace):
args.finetune = False # Don't support finetuning
training_files = [
get_validated_path(f, "data", DEFAULT_DATA_PATH) for f in args.data
]
story_directory, nlu_data_directory = data.get_core_nlu_directories(
training_files)
if not os.listdir(story_directory) or not os.listdir(nlu_data_directory):
print_error(
"Cannot train initial Rasa model. Please provide NLU data and Core data."
)
exit(1)
zipped_model = train.train(args)
perform_interactive_learning(args, zipped_model)
