czbenchmarks.tasks.label_prediction
===================================

.. py:module:: czbenchmarks.tasks.label_prediction


Attributes
----------

.. autoapisummary::

   czbenchmarks.tasks.label_prediction.logger


Classes
-------

.. autoapisummary::

   czbenchmarks.tasks.label_prediction.MetadataLabelPredictionTask


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

.. py:data:: logger

.. py:class:: MetadataLabelPredictionTask(label_key: str, n_folds: int = N_FOLDS, random_seed: int = RANDOM_SEED, min_class_size: int = MIN_CLASS_SIZE)

   Bases: :py:obj:`czbenchmarks.tasks.base.BaseTask`


   Task for predicting labels from embeddings using cross-validation.

   Evaluates multiple classifiers (Logistic Regression, KNN) using k-fold
   cross-validation. Reports standard classification metrics.

   :param label_key: Key to access ground truth labels in metadata
   :param n_folds: Number of cross-validation folds
   :param random_seed: Random seed for reproducibility
   :param min_class_size: Minimum samples required per class


   .. py:attribute:: label_key


   .. py:attribute:: n_folds
      :value: 5



   .. py:attribute:: random_seed
      :value: 42



   .. py:attribute:: min_class_size
      :value: 10



   .. py:property:: display_name
      :type: str


      A pretty name to use when displaying task results


   .. py:property:: required_inputs
      :type: Set[czbenchmarks.datasets.DataType]


      Required input data types.

      :returns: Set of required input DataTypes (metadata with labels)


   .. py:property:: required_outputs
      :type: Set[czbenchmarks.datasets.DataType]


      Required output data types.

      :returns: required output types from models this task to run  (embedding coordinates)


   .. py:method:: set_baseline(data: czbenchmarks.datasets.BaseDataset)

      Set a baseline embedding using raw gene expression.

      Instead of using embeddings from a model, this method uses the raw gene
      expression matrix as features for classification. This provides a baseline
      performance to compare against model-generated embeddings for classification
      tasks.

      :param data: BaseDataset containing AnnData with gene expression and metadata