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Disentanglix Module

Disentanglix

Bases: BasePipeline

Disentanglix-specific version of the BasePipeline.

This class extends BasePipeline. See the parent class for a full list of attributes and methods.

Additional Attributes

_default_config: Is set to DisentanglixConfig here.

Source code in src/autoencodix/disentanglix.py 
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class Disentanglix(BasePipeline):
    """Disentanglix-specific version of the BasePipeline.

    This class extends BasePipeline. See the parent class for a full list
    of attributes and methods.

    Additional Attributes:
        _default_config: Is set to DisentanglixConfig here.
    """

    def __init__(
        self,
        data: Optional[Union[DataPackage, DatasetContainer]] = None,
        trainer_type: Type[BaseTrainer] = GeneralTrainer,
        dataset_type: Type[BaseDataset] = NumericDataset,
        model_type: Type[BaseAutoencoder] = VarixArchitecture,
        loss_type: Type[BaseLoss] = DisentanglixLoss,
        preprocessor_type: Type[BasePreprocessor] = GeneralPreprocessor,
        visualizer: Type[BaseVisualizer] = GeneralVisualizer,
        evaluator: Optional[Type[BaseEvaluator]] = GeneralEvaluator,
        result: Optional[Result] = None,
        datasplitter_type: Type[DataSplitter] = DataSplitter,
        custom_splits: Optional[Dict[str, np.ndarray]] = None,
        config: Optional[DefaultConfig] = None,
    ) -> None:
        """Initialize Varix pipeline with customizable components.

        See the init method of parent class for a full list of Args.
        """
        self._default_config = DisentanglixConfig()
        super().__init__(
            data=data,
            dataset_type=dataset_type,
            trainer_type=trainer_type,
            model_type=model_type,
            loss_type=loss_type,
            preprocessor_type=preprocessor_type,
            visualizer=visualizer,
            evaluator=evaluator,
            result=result,
            datasplitter_type=datasplitter_type,
            config=config,
            custom_split=custom_splits,
        )

__init__(data=None, trainer_type=GeneralTrainer, dataset_type=NumericDataset, model_type=VarixArchitecture, loss_type=DisentanglixLoss, preprocessor_type=GeneralPreprocessor, visualizer=GeneralVisualizer, evaluator=GeneralEvaluator, result=None, datasplitter_type=DataSplitter, custom_splits=None, config=None)

Initialize Varix pipeline with customizable components.

See the init method of parent class for a full list of Args.

Source code in src/autoencodix/disentanglix.py 
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def __init__(
    self,
    data: Optional[Union[DataPackage, DatasetContainer]] = None,
    trainer_type: Type[BaseTrainer] = GeneralTrainer,
    dataset_type: Type[BaseDataset] = NumericDataset,
    model_type: Type[BaseAutoencoder] = VarixArchitecture,
    loss_type: Type[BaseLoss] = DisentanglixLoss,
    preprocessor_type: Type[BasePreprocessor] = GeneralPreprocessor,
    visualizer: Type[BaseVisualizer] = GeneralVisualizer,
    evaluator: Optional[Type[BaseEvaluator]] = GeneralEvaluator,
    result: Optional[Result] = None,
    datasplitter_type: Type[DataSplitter] = DataSplitter,
    custom_splits: Optional[Dict[str, np.ndarray]] = None,
    config: Optional[DefaultConfig] = None,
) -> None:
    """Initialize Varix pipeline with customizable components.

    See the init method of parent class for a full list of Args.
    """
    self._default_config = DisentanglixConfig()
    super().__init__(
        data=data,
        dataset_type=dataset_type,
        trainer_type=trainer_type,
        model_type=model_type,
        loss_type=loss_type,
        preprocessor_type=preprocessor_type,
        visualizer=visualizer,
        evaluator=evaluator,
        result=result,
        datasplitter_type=datasplitter_type,
        config=config,
        custom_split=custom_splits,
    )