TimeSeriesAUPRC#

TimeSeriesAUPRC(integration='trapezoid', weighted_precision=True, with_scores=False)[source]#

TimeSeriesAUPRC: TimeSeries area under precision recall curve.

This metric is used to evaluate the performan of anomaly detection models calculating the precision and recall across different threshold of predicted anomaly scores using window based method,and then subsequently calculating the Area under precision recall curve. Based on the work in paper _[1] and _[2]

\[ \begin{align}\begin{aligned}Trapezoidal Rule (Default):\\\text{AUPRC} \approx \sum_{i=1}^{N} \frac{(R_i - R_{i-1}) \cdot (P_i + P_{i-1})}{2}\\Left Riemann Sum (Alternative):\\\text{AUPRC} \approx \sum_{i=1}^{N} (R_i - R_{i-1}) \cdot P_{i-1}\\TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection, TMLR, 2023 :\\\left(\frac{\text{gt\_length}-1}{\text{gt\_length}}\right)^{\text{cardinality}-1}\end{aligned}\end{align} \]
Parameters:
integrationstr, optional (default=trapezoid)

This parameter specifies the method used to compute the Area Under the Precision-Recall Curve (AUPRC).

weighted_precision: bool, optional (default=True)

parameter determines whether the precision should be computed in a weighted fashion.

with_scoresbool, optional (default= False)

This parameter determines whether the input is in label-score format. If False, then assumes input format to be Predicted and Actual Events.

Returns:
area: float

calculated metric

References

[1]

  1. Tatbul,T.J. Lee,S. Zdonik,M. Alam,J. Gottschlich.

Precision and recall for time series. Advances in neural information processing systems. .. [Rf9676c9c8196-2] D. Wagner,T. Michels,F.C.F. Schulz,A. Nair,M. Rudolph and M. Kloft. TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection. Transactions on Machine Learning Research (TMLR), (to appear) 2023.

Examples

>>> import numpy as np
>>> from sktime.performance_metrics.detection import TimeSeriesAUPRC
>>> ts_auprc = TimeSeriesAUPRC(with_scores=True)
>>> y_true = np.array([0, 0, 1, 1, 0, 0, 1])
>>> y_pred = np.array([0.1, 0.3, 0.7, 0.8, 0.2, 0.0, 0.9])
>>> area = ts_auprc.evaluate(y_true, y_pred)
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