EventWiseRecall

class dtaianomaly.evaluation.EventWiseRecall

Compute the Event-Wise Recall score [9].

Recall measures the model’s ability to correctly identify all actual anomalies. For the Event-Wise Recall, the true positives and false negatives are considered at the event-level:

• \(TP_e\): the number of ground truth anomalous events that fully or partially overlap with a detected segment.

• \(FN_e\): the number of ground truth anomalous events that do not overlap with a detected segment.

We then compute the Event-Wise Recall as:

\[\text{Event-Wise Recall} = \frac{TP_e}{TP_e + FN_e}\]

See also

EventWisePrecision

Compute the event-wise Precision score.

EventWiseFBeta

Compute the event-wise \(F_\beta\) score.

Examples

>>> from dtaianomaly.evaluation import EventWiseRecall
>>> metric = EventWiseRecall()
>>> 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)
1.0

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.