ncalab.search
=============

.. py:module:: ncalab.search


Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/ncalab/search/search/index


Classes
-------

.. autoapisummary::

   ncalab.search.BasicNCATrainer
   ncalab.search.ParameterSet
   ncalab.search.ParameterSearch


Package Contents
----------------

.. py:class:: BasicNCATrainer(nca: ncalab.models.basicNCA.BasicNCAModel, model_path: Optional[pathlib.Path | pathlib.PosixPath], gradient_clipping: bool = False, lr: Optional[float] = None, lr_gamma: float = 0.99, adam_betas=(0.9, 0.95), batch_repeat: int = 2, max_epochs: int = 200, optimizer_method: str = 'adam', pool: Optional[ncalab.training.pool.Pool] = None)

   Trainer class for any model subclassing BasicNCA.

   :param nca: NCA model instance to train.
   :type nca: ncalab.BasicNCAModel
   :param model_path: Path to saved models. If None, models are not saved, defaults to None.
   :type model_path: Path | PosixPath, optional
   :param gradient_clipping: Whether to clip gradients, defaults to False.
   :type gradient_clipping: bool, optional
   :param lr: Initial learning rate, defaults to 16e-4.
   :type lr: float, optional
   :param lr_gamma: Exponential learning rate decay, defaults to 0.9999.
   :type lr_gamma: float, optional
   :param adam_betas: Beta values for Adam optimizer, defaults to (0.9, 0.95).
   :type adam_betas: tuple, optional
   :param batch_repeat: How often each batch will be duplicated, dfaults to 2.
   :param max_epochs: Maximum number of epochs in training, defaults to 200.
   :param optimizer_method: Optimization method, defaults to 'adam'.
   :type optimizer_method: str, optional
   :param pool: Sample pool object.
   :type pool: ncalab.Pool


   .. py:attribute:: nca


   .. py:attribute:: model_path


   .. py:attribute:: gradient_clipping
      :value: False



   .. py:attribute:: lr_gamma
      :value: 0.99



   .. py:attribute:: adam_betas
      :value: (0.9, 0.95)



   .. py:attribute:: batch_repeat
      :value: 2



   .. py:attribute:: max_epochs
      :value: 200



   .. py:attribute:: optimizer_method
      :value: 'adam'



   .. py:attribute:: pool
      :value: None



   .. py:method:: info() -> str

      Shows a markdown-formatted info string with training parameters.
      Useful for showing info on tensorboard to keep track of parameter changes.

      :returns [str]: Markdown-formatted info string.



   .. py:method:: _train_iteration(x: torch.Tensor, y: torch.Tensor, optimizer: torch.optim.Optimizer, total_batch_iterations: int, summary_writer: Optional[torch.utils.tensorboard.SummaryWriter] = None) -> Tuple[ncalab.prediction.Prediction, Dict[str, torch.Tensor]]

      Run a single training iteration.

      :param x: Input training images.
      :param y: Input training labels.
      :param steps: Number of NCA inference time steps.
      :param optimizer: Optimizer.
      :param total_batch_iterations: Total training batch iterations
      :type total_batch_iterations: int
      :param summary_writer: Tensorboard SummaryWriter
      :type summary_writer: SummaryWriter, optional

      :returns: Predicted image.
      :rtype: Tuple[Prediction, Dict[str, torch.Tensor]]



   .. py:method:: train(dataloader_train: torch.utils.data.DataLoader, dataloader_val: Optional[torch.utils.data.DataLoader] = None, dataloader_test: Optional[torch.utils.data.DataLoader] = None, save_every: Optional[int] = None, summary_writer: Optional[torch.utils.tensorboard.SummaryWriter] = None, plot_function: Optional[ncalab.visualization.Visual] = None, earlystopping: Optional[ncalab.training.earlystopping.EarlyStopping] = None) -> ncalab.training.traininghistory.TrainingHistory

      Execute basic NCA training loop with a single function call.

      :param dataloader_train [DataLoader]: Training DataLoader
      :param dataloader_val [DataLoader]: Validation DataLoader
      :param save_every [int]: How often to save model state (in epochs). Useful for very small datasets, like growing lizard.
      :param summary_writer [SummaryWriter] Tensorboard SummaryWriter. Defaults to None.
      :param plot_function: Plot function override. If None, use model's default. Defaults to None.
      :param earlystopping (EarlyStopping, optional): EarlyStopping object. Defaults to None.

      :returns [TrainingHistory]: TrainingHistory object.



.. py:class:: ParameterSet(**kwargs)

   .. py:attribute:: params


   .. py:attribute:: mutable


   .. py:attribute:: combinations
      :value: []



   .. py:attribute:: index
      :value: 0



   .. py:method:: is_mutable(key)


   .. py:method:: info()


   .. py:method:: next() -> Dict[str, Any]


   .. py:method:: num_combinations()


   .. py:method:: __len__()


   .. py:method:: __next__()


   .. py:method:: __iter__()


.. py:class:: ParameterSearch(device, model_class, model_params: ParameterSet, trainer_params: ParameterSet)

   .. py:attribute:: device


   .. py:attribute:: model_class


   .. py:attribute:: model_params


   .. py:attribute:: trainer_params


   .. py:method:: info() -> str

      Generate information string with a summary of the search to run.



   .. py:method:: search(dataloader_train: torch.utils.data.DataLoader, dataloader_val: Optional[torch.utils.data.DataLoader] = None)

      Run search.

      :param dataloader_train [DataLoader]: Training DataLoader.
      :param dataloader_val [DataLoader]: Validation DataLoader. Defaults to None.



   .. py:method:: __call__(*args, **kwargs)

      Shorthand for running the search.