czbenchmarks.tasks.sequential

Attributes

logger

Classes

SequentialOrganizationTaskInput	Pydantic model for Sequential Organization inputs.
SequentialOrganizationOutput	Output for sequential organization task.
SequentialOrganizationTask	Task for evaluating sequential consistency in embeddings.

Module Contents

czbenchmarks.tasks.sequential.logger

class czbenchmarks.tasks.sequential.SequentialOrganizationTaskInput(/, **data: Any)

Bases: 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.

obs: pandas.DataFrame

input_labels: czbenchmarks.types.ListLike

k: int = 15

normalize: bool = True

adaptive_k: bool = False

class czbenchmarks.tasks.sequential.SequentialOrganizationOutput(/, **data: Any)

Bases: 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.

embedding: czbenchmarks.tasks.types.CellRepresentation

class czbenchmarks.tasks.sequential.SequentialOrganizationTask(*, random_seed: int = RANDOM_SEED)

Bases: 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.

Parameters:

random_seed (int) – Random seed for reproducibility

display_name = 'Sequential Organization'

description = 'Evaluate sequential consistency in embeddings using time point labels and k-NN based metrics.'

input_model