dataeval.selection.Select¶

class dataeval.selection.Select(dataset, selections=None)¶

Dataset wrapper that applies selection criteria for filtering.

Wraps an existing dataset and applies one or more selection filters to create a subset view without modifying the original dataset. Supports chaining multiple selection criteria for complex filtering operations.

Parameters:¶

dataset : AnnotatedDataset[_TDatum]¶

Source dataset to wrap and filter. Must implement AnnotatedDataset interface with indexed access to data tuples.

selections : Selection or Sequence[Selection] or None, default None¶

Selection criteria to apply for filtering the dataset. When None, returns all items from the source dataset. Default None creates unfiltered view for consistent interface.

Examples

>>> from dataeval.selection import ClassFilter, Limit

>>> # Apply selection criteria to the dataset
>>> selections = [Limit(size=5), ClassFilter(classes=[0, 2])]
>>> selected_dataset = Select(dataset, selections=selections)

>>> # View selected dataset information
>>> print(selected_dataset)
Select Dataset
--------------
    Selections: [Limit(size=5), ClassFilter(classes=[0, 2], filter_detections=True)]
    Selected Size: 5

ObjectDetectionDataset(n_images=50, classes=['person', 'car', 'boat', 'plane'])

Notes

Selection criteria are applied in the order provided, allowing for efficient sequential filtering. The wrapper maintains all metadata and interface compatibility with the original dataset.

resolve_indices(indices=None)¶

Return the list of dataset indices after all selections have been applied.

Parameters:¶

indices : int or SourceIndex or Sequence[int | SourceIndex] or None, default None¶

Specific indices from the original dataset to resolve after selection. When None, returns all selected indices.

Returns:¶

The list of selected indices from the original dataset.

Return type:¶

list[int]

property metadata : dataeval.protocols.DatasetMetadata¶

Dataset metadata information including identifier and configuration.