stouputils.data_science.models.abstract_model module

Abstract base class for all model implementations. Defines the interface that all concrete model classes must implement.

Provides abstract methods for core model operations including:

• Class routine management

• Model loading

• Training procedures

• Prediction functionality

• Evaluation metrics

Classes inheriting from AbstractModel must implement all methods.

class AbstractModel(num_classes: int, kfold: int = 0, transfer_learning: str = 'imagenet', **override_params: Any)

Bases: object

Abstract class for all models to copy and implement the methods.

routine_full(dataset: Dataset, verbose: int = 0) → AbstractModel

Function ‘routine_full’ is abstract and must be implemented by a subclass

class_load() → None

Function ‘class_load’ is abstract and must be implemented by a subclass

class_train(dataset: Dataset, verbose: int = 0) → bool

Function ‘class_train’ is abstract and must be implemented by a subclass

class_predict(X_test: Iterable[Any]) → Iterable[Any]

Function ‘class_predict’ is abstract and must be implemented by a subclass

class_evaluate(dataset: Dataset, metrics_names: tuple[str, ...] = (), save_model: bool = False, verbose: int = 0) → bool

Function ‘class_evaluate’ is abstract and must be implemented by a subclass

_fit(model: Any, x: Any, y: Any | None = None, validation_data: tuple[Any, Any] | None = None, shuffle: bool = True, batch_size: int | None = None, epochs: int = 1, callbacks: list[Any] | None = None, class_weight: dict[int, float] | None = None, verbose: int = 0, *args: Any, **kwargs: Any) → Any

Function ‘_fit’ is abstract and must be implemented by a subclass

_get_callbacks() → list[Any]

Function ‘_get_callbacks’ is abstract and must be implemented by a subclass

_get_metrics() → list[Any]

Function ‘_get_metrics’ is abstract and must be implemented by a subclass

_get_optimizer(learning_rate: float = 0.0) → Any

Function ‘_get_optimizer’ is abstract and must be implemented by a subclass

_get_loss() → Any

Function ‘_get_loss’ is abstract and must be implemented by a subclass

_get_base_model() → Any

Function ‘_get_base_model’ is abstract and must be implemented by a subclass

_get_architectures(optimizer: Any = None, loss: Any = None, metrics: list[Any] | None = None) → tuple[Any, Any]

Function ‘_get_architectures’ is abstract and must be implemented by a subclass

_find_best_learning_rate(dataset: Dataset, verbose: int = 0) → float

Function ‘_find_best_learning_rate’ is abstract and must be implemented by a subclass

_train_fold(dataset: Dataset, fold_number: int = 0, mlflow_prefix: str = 'history', verbose: int = 0) → Any

Function ‘_train_fold’ is abstract and must be implemented by a subclass

_log_final_model() → None

Function ‘_log_final_model’ is abstract and must be implemented by a subclass

_find_best_learning_rate_subprocess(dataset: Dataset, queue: multiprocessing.queues.Queue[Any] | None = None, verbose: int = 0) → dict[str, Any] | None

Function ‘_find_best_learning_rate_subprocess’ is abstract and must be implemented by a subclass

_find_best_unfreeze_percentage_subprocess(dataset: Dataset, queue: multiprocessing.queues.Queue[Any] | None = None, verbose: int = 0) → dict[str, Any] | None

Function ‘_find_best_unfreeze_percentage_subprocess’ is abstract and must be implemented by a subclass

_train_subprocess(dataset: Dataset, checkpoint_path: str, temp_dir: TemporaryDirectory[str] | None = None, queue: multiprocessing.queues.Queue[Any] | None = None, verbose: int = 0) → dict[str, Any] | None

Function ‘_train_subprocess’ is abstract and must be implemented by a subclass