AffiliationPrecision

class dtaianomaly.evaluation.AffiliationPrecision[source]

Compute the affiliation-based precision score [17].

The affiliation-metrics will first divide the time domain into a number of so-called affiliations: subsequences that are closest to the ground truth anomaly events. These affiliations do not have a fixed size. Then, the precision is computed within each affiliation as the distance from the predicted anomalous events to the ground truth event. The final precision then equals the average precision across all the affiliations.

See also

AffiliationRecall: Compute the affiliation-based Recall score.
AffiliationFBeta: Compute the affiliation-based \(F_\beta\) score.

Examples

>>> from dtaianomaly.evaluation import AffiliationPrecision
>>> metric = AffiliationPrecision()
>>> 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)
0.6875

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.