czbenchmarks.datasets.base
==========================

.. py:module:: czbenchmarks.datasets.base


Classes
-------

.. autoapisummary::

   czbenchmarks.datasets.base.BaseDataset


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

.. py:class:: BaseDataset(path: str, **kwargs: Any)

   Bases: :py:obj:`abc.ABC`


   Helper class that provides a standard way to create an ABC using
   inheritance.


   .. py:attribute:: path


   .. py:attribute:: kwargs


   .. py:property:: inputs
      :type: Dict[czbenchmarks.datasets.types.DataType, czbenchmarks.datasets.types.DataValue]


      Get the inputs dictionary.


   .. py:property:: outputs
      :type: czbenchmarks.models.types.ModelOutputs


      Get the outputs dictionary.


   .. py:method:: set_input(data_type: czbenchmarks.datasets.types.DataType, value: czbenchmarks.datasets.types.DataValue) -> None

      Safely set an input with type checking.



   .. py:method:: set_output(model_type: czbenchmarks.models.types.ModelType | None, data_type: czbenchmarks.datasets.types.DataType, value: czbenchmarks.datasets.types.DataValue) -> None

      Safely set an output with type checking.
      :param model_type: The type of model associated with the output.
                         This parameter is used to differentiate between outputs
                         from various models. It can be set to `None` if the output
                         is not tied to a specific model type defined in the `ModelType` enum.
      :type model_type: ModelType | None
      :param data_type: Specifies the data type of the output.
      :type data_type: DataType
      :param value: The value to assign to the output.
      :type value: Any



   .. py:method:: get_input(data_type: czbenchmarks.datasets.types.DataType) -> czbenchmarks.datasets.types.DataValue

      Safely get an input with error handling.



   .. py:method:: get_output(model_type: czbenchmarks.models.types.ModelType | None, data_type: czbenchmarks.datasets.types.DataType) -> czbenchmarks.datasets.types.DataValue

      Safely get an output with error handling.
      :param model_type: The type of model associated with the output.
                         This parameter is used to differentiate between outputs
                         from various models. It can be set to `None` if the output
                         is not tied to a specific model type defined in the `ModelType` enum.
      :type model_type: ModelType | None
      :param data_type: Specifies the data type of the output.
      :type data_type: DataType

      :returns: The value of the output.
      :rtype: DataValue



   .. py:method:: validate() -> None

      Validate that all inputs and outputs match their expected types



   .. py:method:: load_data() -> None
      :abstractmethod:


      Load the dataset into memory.

      This method should be implemented by subclasses to load their specific
      data format.
      For example, SingleCellDataset loads an AnnData object from an h5ad
      file.

      The loaded data should be stored as instance attributes that can be
      accessed by other methods.



   .. py:method:: unload_data() -> None
      :abstractmethod:


      Unload the dataset from memory.

      This method should be implemented by subclasses to free memory by
      clearing loaded data.
      For example, SingleCellDataset sets its AnnData object to None.

      This is used to clear memory-intensive data before serialization,
      since serializing large raw data artifacts can be error-prone and
      inefficient.

      Any instance attributes containing loaded data should be cleared or
      set to None.



   .. py:method:: serialize(path: str) -> None

      Serialize this dataset instance to disk using dill.

      :param path: Path where the serialized dataset should be saved



   .. py:method:: deserialize(path: str) -> BaseDataset
      :staticmethod:


      Load a serialized dataset from disk.

      :param path: Path to the serialized dataset file

      :returns: The deserialized dataset instance
      :rtype: BaseDataset