czbenchmarks.tasks.sequential
=============================

.. py:module:: czbenchmarks.tasks.sequential


Attributes
----------

.. autoapisummary::

   czbenchmarks.tasks.sequential.logger


Classes
-------

.. autoapisummary::

   czbenchmarks.tasks.sequential.SequentialOrganizationTaskInput
   czbenchmarks.tasks.sequential.SequentialOrganizationOutput
   czbenchmarks.tasks.sequential.SequentialOrganizationTask


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

.. py:data:: logger

.. py:class:: SequentialOrganizationTaskInput(/, **data: Any)

   Bases: :py:obj:`czbenchmarks.tasks.task.TaskInput`


   Pydantic model for Sequential Organization inputs.

   Create a new model by parsing and validating input data from keyword arguments.

   Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
   validated to form a valid model.

   `self` is explicitly positional-only to allow `self` as a field name.


   .. py:attribute:: obs
      :type:  pandas.DataFrame


   .. py:attribute:: input_labels
      :type:  czbenchmarks.types.ListLike


   .. py:attribute:: k
      :type:  int
      :value: 15



   .. py:attribute:: normalize
      :type:  bool
      :value: True



   .. py:attribute:: adaptive_k
      :type:  bool
      :value: False



.. py:class:: SequentialOrganizationOutput(/, **data: Any)

   Bases: :py:obj:`czbenchmarks.tasks.task.TaskOutput`


   Output for sequential organization task.

   Create a new model by parsing and validating input data from keyword arguments.

   Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
   validated to form a valid model.

   `self` is explicitly positional-only to allow `self` as a field name.


   .. py:attribute:: embedding
      :type:  czbenchmarks.tasks.types.CellRepresentation


.. py:class:: SequentialOrganizationTask(*, random_seed: int = RANDOM_SEED)

   Bases: :py:obj:`czbenchmarks.tasks.task.Task`


   Task for evaluating sequential consistency in embeddings.

   This task computes sequential quality metrics for embeddings using time point labels.
   Evaluates how well embeddings preserve sequential organization between cells.

   :param random_seed: Random seed for reproducibility
   :type random_seed: int


   .. py:attribute:: display_name
      :value: 'Sequential Organization'



   .. py:attribute:: description
      :value: 'Evaluate sequential consistency in embeddings using time point labels and k-NN based metrics.'



   .. py:attribute:: input_model