Precision

class dtaianomaly.evaluation.Precision[source]

Compute the Precision score.

Precision measures how accurately the model identifies anomalies. It reflects the proportion of detected anomalies that are truly abnormal. This is particularly important when the cost of false positives (normal events incorrectly flagged as anomalies) is high.

Mathematically, precision is the ratio of true positives (correctly identified anomalies) to all predicted positives, which includes both true anomalies and false positives (normal events mistakenly flagged as anomalies). It can be expressed as:

\[\text{Precision} = \frac{\text{True Anomalies}}{\text{True Anomalies} + \text{False Positives}}\]

A high precision in anomaly detection indicates that the model generates few false alarms, ensuring that most flagged anomalies are truly abnormal events. However, it does not measure how many anomalies were actually identified.

See also

Recall: Compute the Recall score.
FBeta: Compute the \(F_\beta\) score.

Examples

>>> from dtaianomaly.evaluation import Precision
>>> metric = Precision()
>>> 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.