Source code for gluonts.nursery.anomaly_detection.supervised_metrics.bounded_pr_auc

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#
# Licensed under the Apache License, Version 2.0 (the "License").
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# A copy of the License is located at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
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import numpy as np
from sklearn.metrics import auc


def bounded_pr_auc(
    precisions: np.array, recalls: np.array, lower_bound: float = 0
) -> float:
    """
    Bounded PR AUC --> AUC when recall > lower_bound.

    Parameters
    ----------
    precisions : np.array
        precisions of different thresholds
    recalls : np.array
        recalls of different thresholds
    lower_bound : float
        lower bound of recalls

    Returns
    -------
    bounded PR-AUC : float
    """
    sorted_recalls, sorted_precisions = zip(
        *sorted(zip(recalls, precisions), key=lambda x: (x[0], x[1]))
    )
    arg_num = np.argmax(np.array(sorted_recalls) >= lower_bound)
    pr_auc = auc(sorted_recalls[arg_num:], sorted_precisions[arg_num:])
    return pr_auc