ThresholdMetric

class dtaianomaly.evaluation.ThresholdMetric(thresholder: Thresholding, metric: BinaryMetric)[source]

Apply a Thresholding and BinaryMetric in sequence.

Wrapper to combine a BinaryMetric object with some Thresholding, to make sure that it can take continuous anomaly scores as an input. This is done by first applying some Thresholding to the predicted anomaly scores, after which a BinaryMetric can be computed.

Parameters:

thresholderThresholding: Instance of the desired Thresholding class.
metricBinaryMetric: Instance of the desired Metric class.

Examples

>>> from dtaianomaly.evaluation import ThresholdMetric, Precision
>>> from dtaianomaly.thresholding import FixedCutoffThreshold
>>> metric = ThresholdMetric(FixedCutoffThreshold(0.9), Precision())
>>> y_true = [   0,   0,   0,   1,   1,   0,   0,   0]
>>> y_pred = [0.95, 0.5, 0.4, 0.8, 1.0, 0.7, 0.2, 0.1]
>>> 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.