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.", )