ncalab.training.traininghistory
===============================

.. py:module:: ncalab.training.traininghistory


Classes
-------

.. autoapisummary::

   ncalab.training.traininghistory.TrainingStatus
   ncalab.training.traininghistory.TrainingHistory


Module Contents
---------------

.. py:class:: TrainingStatus(*args, **kwds)

   Bases: :py:obj:`enum.Enum`


   Encodes last status of the training.


   .. py:attribute:: STATUS_NONE
      :value: 0



   .. py:attribute:: STATUS_RUNNING
      :value: 1



   .. py:attribute:: STATUS_DONE
      :value: 2



.. py:class:: TrainingHistory(path: Optional[pathlib.Path | pathlib.PosixPath], metrics: Dict[str, float], current_epoch: int, current_model: ncalab.models.AbstractNCAModel, best_accuracy: float = 0, best_epoch: int = 0, best_model: Optional[ncalab.models.AbstractNCAModel] = None, verbose: bool = True)

   Stores data about the training progress. Populated during training
   with ncalab.training.BasicNCATrainer.

   :param path: Save and load path.
   :type path: Optional[Path  |  PosixPath]
   :param metrics: Dict of validation metrics
   :type metrics: Dict[str, float]
   :param current_epoch: Current training epoch.
   :type current_epoch: int
   :param current_model: Currently trained model.
   :type current_model: AbstractNCAModel
   :param best_accuracy: Best validation accuracy, defaults to 0
   :type best_accuracy: float, optional
   :param best_epoch: Epoch of best validation accuracy, defaults to 0
   :type best_epoch: int, optional
   :param best_model: Model with best validation accuracy, defaults to None
   :type best_model: Optional[AbstractNCAModel], optional
   :param verbose: Whether to print updates of validation accuracy, defaults to True
   :type verbose: bool, optional


   .. py:attribute:: path


   .. py:attribute:: metrics


   .. py:attribute:: current_epoch


   .. py:attribute:: current_model


   .. py:attribute:: best_accuracy
      :value: 0



   .. py:attribute:: best_epoch
      :value: 0



   .. py:attribute:: best_model
      :value: None



   .. py:attribute:: verbose
      :value: True



   .. py:attribute:: created_timestamp


   .. py:attribute:: modified_timestamp


   .. py:attribute:: loss
      :type:  List[float]
      :value: []



   .. py:method:: update(epoch: int, model: ncalab.models.AbstractNCAModel, accuracy: float, overwrite: bool = False)

      Populates history with current iteration's values.

      Automatically recognizes changes in accuracy.

      :param epoch: Current epoch
      :type epoch: int
      :param model: Current model
      :type model: AbstractNCAModel
      :param accuracy: Current accuracy, based on model's validation metric
      :type accuracy: float
      :param overwrite: Whether to overwrite best accuracy even with no improvement, defaults to False
      :type overwrite: bool, optional



   .. py:method:: save()

      Saves history and model checkpoint.



   .. py:method:: to_dict() -> Dict

      Return dict of recorded values

      :return: Dict of recorded values
      :rtype: Dict