def set_convert_arguments(parser: argparse.ArgumentParser,
data_type: Text) -> None:
parser.add_argument(
"-f",
"--format",
default="yaml",
choices=["json", "md", "yaml"],
help="Output format the training data should be converted into. "
"Note: currently training data can be converted to 'yaml' format "
"only from 'md' format",
)
add_data_param(parser, required=True, data_type=data_type)
add_out_param(
parser,
default=DEFAULT_CONVERTED_DATA_PATH,
help_text="File (for `json` and `md`) or existing path (for `yaml`) "
"where to save training data in Rasa format.",
)
parser.add_argument("-l",
"--language",
default="en",
help="Language of data.")
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 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_migrate_arguments(parser: argparse.ArgumentParser) -> None:
"""Sets migrate command arguments."""
add_domain_param(parser)
add_out_param(
parser,
default=DEFAULT_DOMAIN_PATH,
help_text="Path (for `yaml`) where to save migrated domain in Rasa 3.0 format.",
)
def set_train_arguments(parser: argparse.ArgumentParser):
add_data_param(parser)
add_config_param(parser)
add_domain_param(parser)
add_out_param(parser, help_text="Directory where your models should be stored.")
add_augmentation_param(parser)
add_debug_plots_param(parser)
add_dump_stories_param(parser)
add_model_name_param(parser)
add_force_param(parser)
def set_migrate_arguments(parser: argparse.ArgumentParser) -> None:
"""Sets migrate command arguments."""
add_domain_param(parser)
add_out_param(
parser,
default=None,
help_text=
"Path (for `yaml`) where to save migrated domain in Rasa 3.0 format."
"If none is specified, either a `new_domain.yml` file or `new_domain` folder "
"will be created in the folder that contains the given domain.",
)
def set_train_arguments(parser: argparse.ArgumentParser):
add_data_param(parser)
add_config_param(parser)
add_domain_param(parser)
add_out_param(parser)
add_augmentation_param(parser)
add_debug_plots_param(parser)
add_dump_stories_param(parser)
add_model_name_param(parser)
add_force_param(parser)
add_compress_param(parser)
def add_test_core_argument_group(
parser: Union[argparse.ArgumentParser, argparse._ActionsContainer],
include_e2e_argument: bool = False,
) -> None:
add_stories_param(parser, "test")
parser.add_argument(
"--max-stories", type=int, help="Maximum number of stories to test on."
)
add_out_param(
parser,
default=DEFAULT_RESULTS_PATH,
help_text="Output path for any files created during the evaluation.",
)
if include_e2e_argument:
parser.add_argument(
"--e2e",
"--end-to-end",
action="store_true",
help="Run an end-to-end evaluation for combined action and "
"intent prediction. Requires a story file in end-to-end "
"format.",
)
add_endpoint_param(
parser, help_text="Configuration file for the connectors as a yml file."
)
parser.add_argument(
"--fail-on-prediction-errors",
action="store_true",
help="If a prediction error is encountered, an exception "
"is thrown. This can be used to validate stories during "
"tests, e.g. on travis.",
)
parser.add_argument(
"--url",
type=str,
help="If supplied, downloads a story file from a URL and "
"trains on it. Fetches the data by sending a GET request "
"to the supplied URL.",
)
parser.add_argument(
"--evaluate-model-directory",
default=False,
action="store_true",
help="Should be set to evaluate models trained via "
"'rasa train core --config <config-1> <config-2>'. "
"All models in the provided directory are evaluated "
"and compared against each other.",
)
add_no_plot_param(parser)
add_errors_success_params(parser)
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_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 set_train_core_arguments(parser: argparse.ArgumentParser):
add_stories_param(parser)
add_domain_param(parser)
add_core_config_param(parser)
add_out_param(parser, help_text="Directory where your models should be stored.")
add_augmentation_param(parser)
add_debug_plots_param(parser)
add_dump_stories_param(parser)
add_force_param(parser)
add_model_name_param(parser)
compare_arguments = parser.add_argument_group("Comparison Arguments")
add_compare_params(compare_arguments)
def set_test_arguments(parser: argparse.ArgumentParser) -> None:
add_model_param(parser, add_positional_arg=False)
core_arguments = parser.add_argument_group("Core Test Arguments")
add_test_core_argument_group(core_arguments)
nlu_arguments = parser.add_argument_group("NLU Test Arguments")
add_test_nlu_argument_group(nlu_arguments)
add_no_plot_param(parser)
add_errors_success_params(parser)
add_out_param(
parser,
default=DEFAULT_RESULTS_PATH,
help_text="Output path for any files created during the evaluation.",
)
def set_train_core_arguments(parser: argparse.ArgumentParser):
add_stories_param(parser)
add_domain_param(parser)
add_core_config_param(parser)
add_out_param(parser)
add_augmentation_param(parser)
add_debug_plots_param(parser)
add_dump_stories_param(parser)
add_force_param(parser)
add_model_name_param(parser)
add_compress_param(parser)
compare_arguments = parser.add_argument_group("Comparison Arguments")
add_compare_params(compare_arguments)
def set_train_core_arguments(parser: argparse.ArgumentParser) -> None:
"""Specifies CLI arguments for `rasa train core`."""
add_stories_param(parser)
add_domain_param(parser)
_add_core_config_param(parser)
add_out_param(parser,
help_text="Directory where your models should be stored.")
add_augmentation_param(parser)
add_debug_plots_param(parser)
add_force_param(parser)
_add_model_name_param(parser)
compare_arguments = parser.add_argument_group("Comparison Arguments")
_add_compare_params(compare_arguments)
add_finetune_params(parser)
def set_train_arguments(parser: argparse.ArgumentParser) -> None:
"""Specifies CLI arguments for `rasa train`."""
add_data_param(parser)
add_config_param(parser)
add_domain_param(parser)
add_out_param(parser,
help_text="Directory where your models should be stored.")
add_dry_run_param(parser)
add_augmentation_param(parser)
add_debug_plots_param(parser)
_add_num_threads_param(parser)
_add_model_name_param(parser)
add_persist_nlu_data_param(parser)
add_force_param(parser)
add_finetune_params(parser)
def set_convert_arguments(parser: argparse.ArgumentParser):
add_data_param(parser, required=True, default=None, data_type="Rasa NLU ")
add_out_param(
parser,
required=True,
default=None,
help_text="File where to save training data in Rasa format.",
)
parser.add_argument("-l", "--language", default="en", help="Language of data.")
parser.add_argument(
"-f",
"--format",
required=True,
choices=["json", "md"],
help="Output format the training data should be converted into.",
)
def add_test_core_argument_group(
parser: Union[argparse.ArgumentParser, argparse._ActionsContainer]
):
add_stories_param(parser, "test")
parser.add_argument(
"--max-stories", type=int, help="Maximum number of stories to test on."
)
add_out_param(
parser,
default="results",
help_text="Output path for any files created during the evaluation.",
)
parser.add_argument(
"--e2e",
"--end-to-end",
action="store_true",
help="Run an end-to-end evaluation for combined action and "
"intent prediction. Requires a story file in end-to-end "
"format.",
)
add_endpoint_param(
parser, help_text="Configuration file for the connectors as a yml file."
)
parser.add_argument(
"--fail-on-prediction-errors",
action="store_true",
help="If a prediction error is encountered, an exception "
"is thrown. This can be used to validate stories during "
"tests, e.g. on travis.",
)
parser.add_argument(
"--NLUHttpInterpreter",
action="store_true",
help="This flag is for enabling Rasa NLU Http interpreter",
)
parser.add_argument(
"--url",
type=str,
help="If supplied, downloads a story file from a URL and "
"trains on it. Fetches the data by sending a GET request "
"to the supplied URL.",
)
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 set_convert_arguments(parser: argparse.ArgumentParser, data_type: Text):
add_data_param(parser, required=True, default=None, data_type=data_type)
add_out_param(
parser,
required=True,
default=None,
help_text="File where to save training data in Rasa format.",
)
parser.add_argument("-l",
"--language",
default="en",
help="Language of data.")
parser.add_argument(
"-f",
"--format",
required=True,
choices=["json", "md", "yaml"],
help="Output format the training data should be converted into. "
"Note: currently training data can be converted to 'yaml' format "
"only from 'md' format",
)
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.")
add_out_param(
parser,
default=DEFAULT_RESULTS_PATH,
help_text="Output path for any files created during the evaluation.",
)
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],
help="Percentages of training data to exclude during comparison.",
)
def _add_data_convert_parsers(data_subparsers,
parents: List[argparse.ArgumentParser]) -> None:
convert_parser = data_subparsers.add_parser(
"convert",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
parents=parents,
help="Converts Rasa data between different formats.",
)
convert_parser.set_defaults(func=lambda _: convert_parser.print_help(None))
convert_subparsers = convert_parser.add_subparsers()
convert_nlu_parser = convert_subparsers.add_parser(
"nlu",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
parents=parents,
help="Converts NLU data between formats.",
)
convert_nlu_parser.set_defaults(func=_convert_nlu_data)
arguments.set_convert_arguments(convert_nlu_parser, data_type="Rasa NLU")
convert_nlg_parser = convert_subparsers.add_parser(
"nlg",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
parents=parents,
help=
("Converts NLG data between formats. If you're migrating from 1.x, "
"please run `rasa data convert responses` to adapt the training data "
"to the new response selector format."),
)
convert_nlg_parser.set_defaults(func=_convert_nlg_data)
arguments.set_convert_arguments(convert_nlg_parser, data_type="Rasa NLG")
convert_core_parser = convert_subparsers.add_parser(
"core",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
parents=parents,
help="Converts Core data between formats.",
)
convert_core_parser.set_defaults(func=_convert_core_data)
arguments.set_convert_arguments(convert_core_parser, data_type="Rasa Core")
migrate_config_parser = convert_subparsers.add_parser(
"config",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
parents=parents,
help="Migrate model configuration between Rasa Open Source versions.",
)
migrate_config_parser.set_defaults(func=_migrate_model_config)
default_arguments.add_config_param(migrate_config_parser)
default_arguments.add_domain_param(migrate_config_parser)
default_arguments.add_out_param(
migrate_config_parser,
default=os.path.join(DEFAULT_DATA_PATH, "rules.yml"),
help_text=
"Path to the file which should contain any rules which are created "
"as part of the migration. If the file doesn't exist, it will be created.",
)
convert_responses_parser = convert_subparsers.add_parser(
"responses",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
parents=parents,
help=
("Convert retrieval intent responses between Rasa Open Source versions. "
"Please also run `rasa data convert nlg` to convert training data files "
"to the right format."),
)
convert_responses_parser.set_defaults(func=_migrate_responses)
arguments.set_convert_arguments(convert_responses_parser,
data_type="Rasa stories")
default_arguments.add_domain_param(convert_responses_parser)