def test_pass_conversation_id_to_interactive_learning(
monkeypatch: MonkeyPatch):
from rasa.core.train import do_interactive_learning
from rasa.core.training import interactive as interactive_learning
parser = argparse.ArgumentParser()
sub_parser = parser.add_subparsers()
interactive.add_subparser(sub_parser, [])
expected_conversation_id = "🎁"
args = parser.parse_args([
"interactive",
"--conversation-id",
expected_conversation_id,
"--skip-visualization",
])
_serve_application = Mock()
monkeypatch.setattr(interactive_learning, "_serve_application",
_serve_application)
do_interactive_learning(args, Mock())
_serve_application.assert_called_once_with(ANY, ANY, True,
expected_conversation_id, 5005)
def test_no_interactive_without_core_data(default_stack_config: Text,
monkeypatch: MonkeyPatch) -> None:
parser = argparse.ArgumentParser()
sub_parser = parser.add_subparsers()
interactive.add_subparser(sub_parser, [])
args = parser.parse_args([
"interactive",
"--config",
default_stack_config,
"--data",
"examples/moodbot/data/nlu.md",
])
interactive._set_not_required_args(args)
mock = Mock()
monkeypatch.setattr(train, "train", mock.train_model)
monkeypatch.setattr(interactive, "perform_interactive_learning",
mock.perform_interactive_learning)
with pytest.raises(SystemExit):
interactive.interactive(args)
mock.train_model.assert_not_called()
mock.perform_interactive_learning.assert_not_called()
def test_train_core_called_when_no_model_passed_and_core(
default_stack_config: Text, monkeypatch: MonkeyPatch) -> None:
parser = argparse.ArgumentParser()
sub_parser = parser.add_subparsers()
interactive.add_subparser(sub_parser, [])
args = parser.parse_args([
"interactive",
"core",
"--config",
default_stack_config,
"--stories",
"examples/moodbot/data/stories.yml",
"--domain",
"examples/moodbot/domain.yml",
])
interactive._set_not_required_args(args)
# Mock actual training and interactive learning methods
mock = Mock()
monkeypatch.setattr(train, "train_core", mock.train_core)
monkeypatch.setattr(interactive, "perform_interactive_learning",
mock.perform_interactive_learning)
interactive.interactive(args)
mock.train_core.assert_called_once()
def create_argument_parser() -> argparse.ArgumentParser:
"""Parse all the command line arguments for the training script."""
parser = argparse.ArgumentParser(
prog="rasa",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Rasa command line interface. Rasa allows you to build "
"your own conversational assistants 🤖. The 'rasa' command "
"allows you to easily run most common commands like "
"creating a new bot, training or evaluating models.")
parser.add_argument("--version",
action='store_true',
default=argparse.SUPPRESS,
help="Print installed Rasa version")
parent_parser = argparse.ArgumentParser(add_help=False)
add_logging_option_arguments(parent_parser)
parent_parsers = [parent_parser]
subparsers = parser.add_subparsers(help='Rasa commands')
scaffold.add_subparser(subparsers, parents=parent_parsers)
run.add_subparser(subparsers, parents=parent_parsers)
shell.add_subparser(subparsers, parents=parent_parsers)
train.add_subparser(subparsers, parents=parent_parsers)
interactive.add_subparser(subparsers, parents=parent_parsers)
test.add_subparser(subparsers, parents=parent_parsers)
show.add_subparser(subparsers, parents=parent_parsers)
data.add_subparser(subparsers, parents=parent_parsers)
up.add_subparser(subparsers, parents=parent_parsers)
return parser
def test_generate_conversation_id_for_interactive_learning(monkeypatch: MonkeyPatch):
parser = argparse.ArgumentParser()
sub_parser = parser.add_subparsers()
interactive.add_subparser(sub_parser, [])
args = parser.parse_args(["interactive"])
assert args.conversation_id
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 test_no_interactive_without_core_data(stack_config_path: Text,
monkeypatch: MonkeyPatch,
nlu_data_path: Text) -> None:
parser = argparse.ArgumentParser()
sub_parser = parser.add_subparsers()
interactive.add_subparser(sub_parser, [])
args = parser.parse_args([
"interactive", "--config", stack_config_path, "--data", nlu_data_path
])
interactive._set_not_required_args(args)
mock = Mock()
monkeypatch.setattr(train, "run_training", mock.train_model)
monkeypatch.setattr(interactive, "perform_interactive_learning",
mock.perform_interactive_learning)
with pytest.raises(SystemExit):
interactive.interactive(args)
mock.train_model.assert_not_called()
mock.perform_interactive_learning.assert_not_called()
def test_train_called_when_no_model_passed(stack_config_path: Text,
monkeypatch: MonkeyPatch) -> None:
parser = argparse.ArgumentParser()
sub_parser = parser.add_subparsers()
interactive.add_subparser(sub_parser, [])
args = parser.parse_args([
"interactive",
"--config",
stack_config_path,
"--data",
"examples/moodbot/data",
])
interactive._set_not_required_args(args)
# Mock actual training and interactive learning methods
mock = Mock()
monkeypatch.setattr(train, "run_training", mock.train_model)
monkeypatch.setattr(interactive, "perform_interactive_learning",
mock.perform_interactive_learning)
interactive.interactive(args)
mock.train_model.assert_called_once()
def create_argument_parser() -> argparse.ArgumentParser:
"""Parse all the command line arguments for the training script."""
parser = argparse.ArgumentParser(
prog="rasa",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Rasa command line interface. Rasa allows you to build "
"your own conversational assistants 🤖. The 'rasa' command "
"allows you to easily run most common commands like "
"creating a new bot, training or evaluating models.",
)
parser.add_argument(
"--version",
action="store_true",
default=argparse.SUPPRESS,
help="Print installed Rasa version",
)
parent_parser = argparse.ArgumentParser(add_help=False) # 一级命令解析器
add_logging_options(parent_parser)
parent_parsers = [parent_parser]
subparsers = parser.add_subparsers(help="Rasa commands") # 二级命令解析器
scaffold.add_subparser(subparsers, parents=parent_parsers) # 解析rasa init
run.add_subparser(subparsers, parents=parent_parsers) # 解析rasa run
shell.add_subparser(subparsers, parents=parent_parsers) # 解析rasa shell
train.add_subparser(subparsers, parents=parent_parsers) # 解析 rasa train
interactive.add_subparser(subparsers, parents=parent_parsers) # 解析rasa interactive
test.add_subparser(subparsers, parents=parent_parsers) # 解析rasa test
visualize.add_subparser(subparsers, parents=parent_parsers) # 解析rasa visualize
data.add_subparser(subparsers, parents=parent_parsers) # 解析rasa data
x.add_subparser(subparsers, parents=parent_parsers) # 解析rasa x
return parser
