def set_train_nlu_arguments(parser: argparse.ArgumentParser):
add_config_param(parser)
add_out_param(parser, help_text="Directory where your models should be stored.")
add_nlu_data_param(parser, help_text="File or folder containing your NLU data.")
add_model_name_param(parser)
def add_test_nlu_argument_group(parser: Union[argparse.ArgumentParser,
argparse._ActionsContainer]):
add_nlu_data_param(parser)
parser.add_argument(
"--report",
required=False,
nargs="?",
const="reports",
default=None,
help="Output path to save the intent/entity metrics report.",
)
parser.add_argument(
"--successes",
required=False,
nargs="?",
const="successes.json",
default=None,
help="Output path to save successful predictions.",
)
parser.add_argument(
"--errors",
required=False,
default="errors.json",
help="Output path to save model errors.",
)
parser.add_argument(
"--histogram",
required=False,
default="hist.png",
help="Output path for the confidence histogram.",
)
parser.add_argument(
"--confmat",
required=False,
default="confmat.png",
help="Output path for the confusion matrix plot.",
)
cross_validation_arguments = parser.add_argument_group("Cross Validation")
cross_validation_arguments.add_argument(
"--cross-validation",
action="store_true",
default=False,
help=
"Switch on cross validation mode. Any provided model will be ignored.",
)
cross_validation_arguments.add_argument(
"-c",
"--config",
type=str,
default=DEFAULT_CONFIG_PATH,
help="Model configuration file (cross validation only).",
)
cross_validation_arguments.add_argument(
"-f",
"--folds",
required=False,
default=10,
help="Number of cross validation folds (cross validation only).",
)
def set_train_nlu_arguments(parser: argparse.ArgumentParser):
add_config_param(parser)
add_out_param(parser)
add_nlu_data_param(parser)
add_model_name_param(parser)
add_compress_param(parser)
def add_test_nlu_argument_group(
parser: Union[argparse.ArgumentParser,
argparse._ActionsContainer]) -> None:
add_nlu_data_param(parser,
help_text="File or folder containing your NLU data.")
add_out_param(
parser,
default=DEFAULT_RESULTS_PATH,
help_text="Output path for any files created during the evaluation.",
)
parser.add_argument(
"-c",
"--config",
nargs="+",
default=None,
help="Model configuration file. If a single file is passed and cross "
"validation mode is chosen, cross-validation is performed, if "
"multiple configs or a folder of configs are passed, models "
"will be trained and compared directly.",
)
cross_validation_arguments = parser.add_argument_group("Cross Validation")
cross_validation_arguments.add_argument(
"--cross-validation",
action="store_true",
default=False,
help=
"Switch on cross validation mode. Any provided model will be ignored.",
)
cross_validation_arguments.add_argument(
"-f",
"--folds",
required=False,
default=5,
help="Number of cross validation folds (cross validation only).",
)
comparison_arguments = parser.add_argument_group("Comparison Mode")
comparison_arguments.add_argument(
"-r",
"--runs",
required=False,
default=3,
type=int,
help="Number of comparison runs to make.",
)
comparison_arguments.add_argument(
"-p",
"--percentages",
required=False,
nargs="+",
type=int,
default=[0, 25, 50, 75],
help="Percentages of training data to exclude during comparison.",
)
add_no_plot_param(parser)
add_errors_success_params(parser)
def set_train_nlu_arguments(parser: argparse.ArgumentParser) -> None:
"""Specifies CLI arguments for `rasa train nlu`."""
add_config_param(parser)
add_domain_param(parser, default=None)
add_out_param(parser,
help_text="Directory where your models should be stored.")
add_nlu_data_param(parser,
help_text="File or folder containing your NLU data.")
_add_num_threads_param(parser)
_add_model_name_param(parser)
add_persist_nlu_data_param(parser)
add_finetune_params(parser)
def _add_split_args(parser: argparse.ArgumentParser) -> None:
add_nlu_data_param(parser)
parser.add_argument(
"--training-fraction",
type=float,
default=0.8,
help="Percentage of the data which should be the training data",
)
parser.add_argument(
"-o",
"--out",
type=str,
default="train_test_split",
help="Directory where the split files should be stored",
)
def set_split_arguments(parser: argparse.ArgumentParser):
add_nlu_data_param(parser, help_text="File or folder containing your NLU data.")
parser.add_argument(
"--training-fraction",
type=float,
default=0.8,
help="Percentage of the data which should be in the training data.",
)
add_out_param(
parser,
default="train_test_split",
help_text="Directory where the split files should be stored.",
)
def set_visualize_stories_arguments(parser: argparse.ArgumentParser):
add_domain_param(parser)
add_stories_param(parser)
add_config_param(parser)
add_out_param(
parser,
default="graph.html",
help_text="Filename of the output path, e.g. 'graph.html'.",
)
parser.add_argument(
"--max-history",
default=2,
type=int,
help="Max history to consider when merging paths in the output graph.",
)
add_nlu_data_param(
parser,
default=None,
help_text="File or folder containing your NLU data, "
"used to insert example messages into the graph.",
)
def add_subparser(subparsers: argparse._SubParsersAction,
parents: List[argparse.ArgumentParser]):
import rasa.cli.arguments.train as core_cli
train_parser = subparsers.add_parser("train", help="Train the Rasa bot")
train_subparsers = train_parser.add_subparsers()
train_core_parser = train_subparsers.add_parser(
"core",
conflict_handler="resolve",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
help="Train Rasa Core",
)
train_core_parser.set_defaults(func=train_core)
train_nlu_parser = train_subparsers.add_parser(
"nlu",
parents=parents,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
help="Train Rasa NLU",
)
train_nlu_parser.set_defaults(func=train_nlu)
for p in [train_parser, train_core_parser, train_nlu_parser]:
add_general_arguments(p)
for p in [train_core_parser, train_parser]:
add_domain_param(p)
core_cli.add_general_args(p)
add_stories_param(train_core_parser)
_add_core_compare_arguments(train_core_parser)
add_nlu_data_param(train_nlu_parser)
add_joint_parser_arguments(train_parser)
train_parser.set_defaults(func=train)
def add_test_nlu_argument_group(
parser: Union[argparse.ArgumentParser, argparse._ActionsContainer]
):
add_nlu_data_param(parser, help_text="File or folder containing your NLU data.")
parser.add_argument(
"--report",
required=False,
nargs="?",
const="reports",
default=None,
help="Output path to save the intent/entity metrics report.",
)
parser.add_argument(
"--successes",
required=False,
nargs="?",
const="successes.json",
default=None,
help="Output path to save successful predictions.",
)
parser.add_argument(
"--errors",
required=False,
default="errors.json",
help="Output path to save model errors.",
)
parser.add_argument(
"--histogram",
required=False,
default="hist.png",
help="Output path for the confidence histogram.",
)
parser.add_argument(
"--confmat",
required=False,
default="confmat.png",
help="Output path for the confusion matrix plot.",
)
parser.add_argument(
"-c",
"--config",
nargs="+",
default=None,
help="Model configuration file. If a single file is passed and cross "
"validation mode is chosen, cross-validation is performed, if "
"multiple configs or a folder of configs are passed, models "
"will be trained and compared directly.",
)
cross_validation_arguments = parser.add_argument_group("Cross Validation")
cross_validation_arguments.add_argument(
"--cross-validation",
action="store_true",
default=False,
help="Switch on cross validation mode. Any provided model will be ignored.",
)
cross_validation_arguments.add_argument(
"-f",
"--folds",
required=False,
default=10,
help="Number of cross validation folds (cross validation only).",
)
comparison_arguments = parser.add_argument_group("Comparison Mode")
comparison_arguments.add_argument(
"-r",
"--runs",
required=False,
default=3,
type=int,
help="Number of comparison runs to make.",
)
comparison_arguments.add_argument(
"-p",
"--percentages",
required=False,
nargs="+",
type=int,
default=[0, 25, 50, 75, 90],
help="Percentages of training data to exclude during comparison.",
)
