unweighted_average_precision()

audmetric.unweighted_average_precision(truth, prediction, labels=None, *, zero_division=0)[source]

Unweighted average precision.

UAP=1K∑k=1Ktrue positivektrue positivek+false positivek\text{UAP} = \frac{1}{K} \sum^K_{k=1} \frac{\text{true positive}_k} {\text{true positive}_k + \text{false positive}_k}
Parameters

  • truth (Sequence[Any]) – ground truth values/classes

  • prediction (Sequence[Any]) – predicted values/classes

  • labels (Optional[Sequence[Any]]) – included labels in preferred ordering. If no labels are supplied, they will be inferred from {prediction,truth}\{\text{prediction}, \text{truth}\} and ordered alphabetically.

  • zero_division (float) – set the value to return when there is a zero division

Return type

float

Returns

unweighted average precision

Examples

>>> unweighted_average_precision([0, 0], [0, 1])
0.5