Selection¶

Provides the user python interface to selection bound to sample ids.

Selection ¶

Selection(dataset_id: UUID, session: Session, input_sample_ids: Iterable[UUID])

Selection interface for candidate sample ids.

Parameters:

Name	Type	Description	Default
`dataset_id`	`UUID`	Dataset in which the selection is performed.	required
`session`	`Session`	Database session to resolve selection dependencies.	required
`input_sample_ids`	`Iterable[UUID]`	Candidate sample ids considered for selection. The iterable is consumed immediately to capture a stable snapshot.	required

diverse(
    n_samples_to_select: int,
    selection_result_tag_name: str,
    embedding_model_name: str | None = None,
) -> None

Select a diverse subset using embeddings.

Parameters:

Name	Type	Description	Default
`n_samples_to_select`	`int`	Number of samples to select.	required
`selection_result_tag_name`	`str`	Tag name for the selection result.	required
`embedding_model_name`	`str \| None`	Optional embedding model name. If None, uses the only available model or raises if multiple exist.	`None`

metadata_weighting(
    n_samples_to_select: int, selection_result_tag_name: str, metadata_key: str
) -> None

Select a subset based on numeric metadata weights.

Parameters:

Name	Type	Description	Default
`n_samples_to_select`	`int`	Number of samples to select.	required
`selection_result_tag_name`	`str`	Tag name for the selection result.	required
`metadata_key`	`str`	Metadata key used as weights (float or int values).	required

multi_strategies(
    n_samples_to_select: int,
    selection_result_tag_name: str,
    selection_strategies: list[SelectionStrategy],
) -> None

Select a subset based on multiple strategies.

Parameters:

Name	Type	Description	Default
`n_samples_to_select`	`int`	Number of samples to select.	required
`selection_result_tag_name`	`str`	Tag name for the selection result.	required
`selection_strategies`	`list[SelectionStrategy]`	Strategies to compose for selection.	required