AreaUnderROC

class dtaianomaly.evaluation.AreaUnderROC[source]

Compute the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) score.

The AUC-ROC is a widely used metric to evaluate the performance of a binary classifier, especially in anomaly detection. The ROC-curve plots the true positive rate (recall) against the false positive rate across different classification thresholds. The AUC-ROC represents the likelihood that the model ranks a randomly chosen anomaly higher than a randomly chosen normal instance. AUC-ROC provides a single number summarizing the model’s ability to distinguish between normal and anomalous instances. A value of 1.0 indicates perfect discrimination, while 0.5 implies the model performs no better than random guessing. It is especially useful when anomalies are rare, as it considers the trade-off between detecting true anomalies (high recall) and minimizing false positives.

See also

AreaUnderPR: Compute the Area Under the PR-Curve.

Examples

>>> from dtaianomaly.evaluation import AreaUnderROC
>>> metric = AreaUnderROC()
>>> 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)
0.833...

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.