EventWisePrecision

class dtaianomaly.evaluation.EventWisePrecision[source]

Compute the Event-Wise Precision score [9].

Precision measures how accurately the model identifies anomalies. For the Event-Wise Precision, the true and false positives are considered at the event-level:

\(TP_e\): the number of ground truth anomalous events that fully or partially overlap with a detected segment.
\(FP_e\): the number of detected segments that do not overlap with any ground truth anomalous event.

The precision is corrected by the false-alarm rate (FAR) to avoid a model which predicts all observations as anomalous to have a high score. The FAR is computed on the point-level:

\(FP\): the number of detected anomalous points that are not actually anomalous.

We then compute the Event-Wise Precision as (with \(N\): the total number of normal points):

\[\text{Event-Wise Precision} = \frac{TP_e}{TP_e + FP_e} \times (1 - \frac{FP}{N})\]

See also

EventWiseRecall: Compute the event-wise Recall score.
EventWiseFBeta: Compute the event-wise \(F_\beta\) score.

Examples

>>> from dtaianomaly.evaluation import EventWisePrecision
>>> metric = EventWisePrecision()
>>> y_true = [0, 0, 0, 1, 1, 0, 0, 0]
>>> y_pred = [1, 0, 0, 1, 1, 1, 0, 0]
>>> metric.compute(y_true, y_pred)
0.333...

compute(y_true: ndarray, y_pred: ndarray, **kwargs) → float

Compute the performance score.

Evaluate how closely the given anomaly scores align to the ground truth anomaly scores.

Parameters:

y_truearray-like of shape (n_samples): Ground-truth labels.
y_predarray-like of shape (n_samples): Predicted anomaly scores.
**kwargs: Additional arguments used for computing the evaluation metric.

Returns:

float: The alignment score of the given ground truth and prediction, according to this score.

Raises:

ValueError: When inputs are not numeric “array-like”s
ValueError: If shapes of y_true and y_pred are not of identical shape
ValueError: If y_true is non-binary.
ValueError: If y_pred is non-binary.