ncalab.training.kfold
=====================

.. py:module:: ncalab.training.kfold


Classes
-------

.. autoapisummary::

   ncalab.training.kfold.TrainValRecord
   ncalab.training.kfold.SplitDefinition
   ncalab.training.kfold.KFoldCrossValidationTrainer


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

.. py:class:: TrainValRecord(train: List[str], val: List[str])

   Helper class, storing a training / validation data split to generate
   respective DataLoader objects.

   :param train:  List of training image file paths
   :type train: List[str]
   :param val: List of validation image file paths
   :type val: List[str]


   .. py:attribute:: train


   .. py:attribute:: val


   .. py:method:: dataloaders(DatasetType: Type, path: pathlib.Path | pathlib.PosixPath, transform=None, batch_sizes=None)

      Generate a pair of training and validation DataLoader objects, based on
      a given DataSet subtype.



.. py:class:: SplitDefinition

   Stores a k-fold cross-validation split.


   .. py:attribute:: folds
      :value: []



   .. py:attribute:: dataloader_test
      :value: None



   .. py:method:: read(path: pathlib.PosixPath) -> SplitDefinition
      :staticmethod:


      Reads json files with split definitions, similar to those created by nnUNet.

      Format is like

      .. highlight:: python
      .. code-block:: python

          [
              {
                  "train": [ "filename0", "filename1",... ]
                  "val": [ "filename2", "filename3",... ]
              },
              {
                  ...
              }
          ]

      :param path: Path to JSON file containing split definition.
      :returns: SplitDefinition object
      :rtype: SplitDefinition



   .. py:method:: __len__() -> int


   .. py:method:: __getitem__(idx) -> TrainValRecord


.. py:class:: KFoldCrossValidationTrainer(trainer: ncalab.training.trainer.BasicNCATrainer, split: SplitDefinition)

   
   :param trainer [BasicNCATrainer]: BasicNCATrainer, to train each individual fold.
   :param split [SplitDefinition]: Definition of the split used for k-fold cross-training.


   .. py:attribute:: trainer


   .. py:attribute:: model_prototype


   .. py:attribute:: model_name


   .. py:attribute:: split


   .. py:method:: train(DatasetType: Type, datapath: pathlib.Path | pathlib.PosixPath, transform, batch_sizes: None | Dict = None, save_every: int | None = None) -> List[ncalab.training.traininghistory.TrainingHistory]

      Run training loop with a single function call.

      :param DatasetType [Type]: Type of dataset class to use.
      :param datapath [Path]: _description_
      :param transform: Data transform, e.g. initialized via Albumentations.
      :param batch_sizes: Dict of batch sizes per set, e.g. {"train": 8, "val": 16}. Defaults to None.
      :param save_every [int]: _description_. Defaults to None.
      :param plot_function: Plot function override. If None, use model's default. Defaults to None.

      :returns [List[TrainingHistory]]: List of TrainingHistory objects, one per fold.