library.phases.phases_implementation.modelling.shallow.model_definition.model_states.model_state module

class library.phases.phases_implementation.modelling.shallow.model_definition.model_states.model_state.InTuningState(model_sklearn: object, modelName: str, dataset: Dataset, results_header: list[str], model_type: str = 'classical')

Bases: ModelState

fit(**kwargs)

get_fit_data()

Varies over each state (in post its training + val for instance)

get_predict_data()

plot_convergence()

predict()

class library.phases.phases_implementation.modelling.shallow.model_definition.model_states.model_state.ModelState(model_sklearn: object, modelName: str, model_type: str, dataset: Dataset, results_header: list[str])

Bases: ABC

abstractmethod fit()

abstractmethod get_fit_data()

Varies over each state (in post its training + val for instance)

abstractmethod get_predict_data()

abstractmethod predict(is_training: bool = False)

class library.phases.phases_implementation.modelling.shallow.model_definition.model_states.model_state.PostTuningState(model_sklearn: object, modelName: str, dataset: Dataset, results_header: list[str], model_type: str = 'classical')

Bases: ModelState

fit(**kwargs)

get_fit_data()

Varies over each state (in post its training + val for instance)

get_predict_data()

predict()

class library.phases.phases_implementation.modelling.shallow.model_definition.model_states.model_state.PreTuningState(model_sklearn: object, modelName: str, model_type: str, dataset: Dataset, results_header: list[str])

Bases: ModelState

fit()

get_fit_data()

Varies over each state (in post its training + val for instance)

get_predict_data()

predict()