czbenchmarks.tasks.integration

Attributes

logger

Classes

BatchIntegrationTaskInput	Pydantic model for BatchIntegrationTask inputs.
BatchIntegrationOutput	Output for batch integration task.
BatchIntegrationTask	Task for evaluating batch integration quality.

Module Contents

czbenchmarks.tasks.integration.logger

class czbenchmarks.tasks.integration.BatchIntegrationTaskInput(/, **data: Any)

Bases: czbenchmarks.tasks.task.TaskInput

Pydantic model for BatchIntegrationTask 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.

batch_labels: Annotated[czbenchmarks.types.ListLike, Field(description='Batch labels for each cell  (e.g. `obs.batch` from an AnnData object).')]

labels: Annotated[czbenchmarks.types.ListLike, Field(description='Ground truth labels for metric calculation (e.g. `obs.cell_type` from an AnnData object).')]

class czbenchmarks.tasks.integration.BatchIntegrationOutput(/, **data: Any)

Bases: czbenchmarks.tasks.task.TaskOutput

Output for batch integration 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.

cell_representation: czbenchmarks.tasks.types.CellRepresentation

class czbenchmarks.tasks.integration.BatchIntegrationTask(*, random_seed: int = RANDOM_SEED)

Bases: czbenchmarks.tasks.task.Task

Task for evaluating batch integration quality.

This task computes metrics to assess how well different batches are integrated in the embedding space while preserving biological signals.

display_name = 'Batch Integration'

description = 'Evaluate batch integration quality using various integration metrics.'

input_model

baseline_model