qualia_core.learningframework.LearningFramework module

class qualia_core.learningframework.LearningFramework.LearningFramework

Bases: ABC, Generic[T]

learningmodels: ModuleType

abstract train(model: T, trainset: RawData, validationset: RawData, epochs: int, batch_size: int, optimizer: OptimizerConfigDict | None, dataaugmentations: list[DataAugmentation], experimenttracking: ExperimentTracking | None, name: str) → T

abstract load(name: str, model: T) → T

abstract evaluate(model: T, testset: RawData, batch_size: int, dataaugmentations: list[DataAugmentation], experimenttracking: ExperimentTracking | None, dataset_type: str, name: str) → dict[str, int | float | numpy.typing.NDArray[Any]]

abstract predict(model: T, dataset: RawData, batch_size: int, dataaugmentations: list[DataAugmentation], experimenttracking: ExperimentTracking | None, name: str) → Any

abstract export(model: T, name: str) → None

abstract summary(model: T) → None

abstract n_params(model: T) → int

abstract save_graph_plot(model: T, model_save: str) → None

abstract apply_dataaugmentation(da: DataAugmentation, x: numpy.typing.NDArray[Any], y: numpy.typing.NDArray[Any], **kwargs: Any) → tuple[numpy.typing.NDArray[Any], numpy.typing.NDArray[Any]]