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Varix Module

Varix

Bases: BasePipeline

Varix specific version of the BasePipeline class.

Inherits preprocess, fit, predict, evaluate, and visualize methods from BasePipeline. This class extends BasePipeline. See the parent class for a full list of attributes and methods.

Additional Attributes

_default_config: Is set to VarixConfig here.

Source code in src/autoencodix/varix.py 
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class Varix(BasePipeline):
    """Varix specific version of the BasePipeline class.

    Inherits preprocess, fit, predict, evaluate, and visualize methods from BasePipeline.
    This class extends BasePipeline. See the parent class for a full list
    of attributes and methods.

    Additional Attributes:
        _default_config: Is set to VarixConfig 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] = VarixLoss,
        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,
        ontologies: Optional[Union[List, Dict]] = None,
        config: Optional[DefaultConfig] = None,
    ) -> None:
        """Initialize Varix pipeline with customizable components.

        Some components are passed as types rather than instances because they require
        data that is only available after preprocessing.

        See parent class for full list of Args.

        """
        self._default_config = VarixConfig()
        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,
            ontologies=ontologies,
        )

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

Initialize Varix pipeline with customizable components.

Some components are passed as types rather than instances because they require data that is only available after preprocessing.

See parent class for full list of Args.

Source code in src/autoencodix/varix.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] = VarixLoss,
    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,
    ontologies: Optional[Union[List, Dict]] = None,
    config: Optional[DefaultConfig] = None,
) -> None:
    """Initialize Varix pipeline with customizable components.

    Some components are passed as types rather than instances because they require
    data that is only available after preprocessing.

    See parent class for full list of Args.

    """
    self._default_config = VarixConfig()
    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,
        ontologies=ontologies,
    )