PointAdjustedPrecision

class dtaianomaly.evaluation.PointAdjustedPrecision[source]

Compute the point-adjusted precision.

For given binary anomaly predictions and ground truth anomaly labels, point-adjusting will treat any sequence of consecutive ground truth anomalies as anomalous events. If any of the observations in such an event has been detected, then we say that the anomaly has been detected. In this case, all predictions in the anomalous event are set to 1, thereby indicating that the method predicted an anomaly.

Warning

It is known that the point-adjusted metrics heavily overestimate the performance of anomaly detectors. It is therefore not recommended to solely rely on those metrics to evaluate a model. These metrics were implemented for reproducibility of existing works.

See also

Precision: Compute the standard, not point-adjusted precision.

Examples

>>> from dtaianomaly.evaluation import PointAdjustedPrecision
>>> metric = PointAdjustedPrecision()
>>> 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)
0.5

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.