fiftyone.core.aggregations.FacetAggregations

Efficiently computes a set of aggregations rooted at a common path using faceted computations.

Examples:

import fiftyone as fo

dataset = fo.Dataset()
dataset.add_samples(
    [
        fo.Sample(
            filepath="/path/to/image1.png",
            tags=["sunny"],
            predictions=fo.Detections(
                detections=[
                    fo.Detection(label="cat", confidence=0.4),
                    fo.Detection(label="dog", confidence=0.5),
                ]
            ),
        ),
        fo.Sample(
            filepath="/path/to/image2.png",
            tags=["sunny", "cloudy"],
            predictions=fo.Detections(
                detections=[
                    fo.Detection(label="cat", confidence=0.6),
                    fo.Detection(label="rabbit", confidence=0.7),
                ]
            ),
        ),
        fo.Sample(
            filepath="/path/to/image3.png",
            predictions=None,
        ),
    ]
)

#
# Compute prediction label value counts and confidence bounds
#

values, bounds = dataset.aggregate(
    fo.FacetAggregations(
        "predictions.detections",
        [fo.CountValues("label"), fo.Bounds("confidence")]
    )
)
print(values)  # label value counts
print(bounds)  # confidence bounds