[Feature request] Nested argument parser, e.g., "--model.name MODEL_NAME --model.params.lambda 0.1".

Thank you for your great repo!

I've used this package in nearly all of my projects. Gradually, I found that the most urgent and useful feature will be a nested argument parser. That is:

python my_app.py --arg1.subarg1.subsubarg1 xxx --arg1.subarg2 xxx --arg2.subarg1 xxx

I don't know whether there is a workaround. Or do you have any plans to add this feature?:smile:

Currently, my workaround is similar to this:

class TrainArgs(Tap):
    tr_ctn: bool = None  #: Flag for training continue.
    ctn_epoch: int = None  #: Start epoch for continue training.

class DatasetArgs(Tap):
    dataset_name: str = None  #: Name of the chosen dataset.
    dataloader_name: str = None  #: Name of the chosen dataloader. The default is BaseDataLoader.

class ModelArgs(Tap):
    r"""
    Correspond to ``model`` configs in config files.
    """
    model_name: str = None  #: Name of the chosen GNN.
    model_layer: int = None  #: Number of the GNN layer.

class OODArgs(Tap):
    r"""
    Correspond to ``ood`` configs in config files.
    """
    ood_alg: str = None  #: Name of the chosen OOD algorithm.
    ood_param: float = None  #: OOD algorithms' hyperparameter(s). Currently, most of algorithms use it as a float value.

class AutoArgs(Tap):
    config_root: str = None  #: The root of input configuration files.
    launcher: str = None  #: The launcher name.

class CommonArgs(Tap):
    r"""
    Correspond to general configs in config files.
    """
    config_path: str = None  #: (Required) The path for the config file.
    task: Literal['train', 'test'] = None  #: Running mode. Allowed: 'train' and 'test'.

    # For code auto-complete
    train: TrainArgs = None  #: For code auto-complete
    model: ModelArgs = None  #: For code auto-complete
    dataset: DatasetArgs = None  #: For code auto-complete
    ood: OODArgs = None  #: For code auto-complete

    def process_args(self) -> None:
        super().process_args()

        self.dataset = DatasetArgs().parse_args(args=self.argv, known_only=True)
        self.train = TrainArgs().parse_args(args=self.argv, known_only=True)
        self.model = ModelArgs().parse_args(args=self.argv, known_only=True)
        self.ood = OODArgs().parse_args(args=self.argv, known_only=True)

def args_parser(argv: list=None):
    common_args = CommonArgs(argv=argv).parse_args(args=argv, known_only=True)
    return common_args

Therefore, I can use something like args.model.model_name in my code with autocomplete. But I cannot parse them in a similar way from the argv, e.g., --model.model_name XXX.

swansonk14 / typed-argument-parser

[Feature request] Nested argument parser, e.g., "--model.name MODEL_NAME --model.params.lambda 0.1". #102