04cd555be76efc7baced753c751c4257d41eb75d,EvalMetrics.py,,ErrorRateAt95Recall,#,10

Before Change

    sorted_scores.sort(key=operator.itemgetter(1), reverse=False)

    // Compute error rate
    n_match = sum(1 for x in sorted_scores if x[0] == 1)
    n_thresh = recall_point * n_match
    tp = 0
    count = 0
    for label, score in sorted_scores:

After Change

    FP = np.sum(labels[:threshold_index] == 0) // Below threshold (i.e., labelled positive), but should be negative
    TN = np.sum(labels[threshold_index:] == 0) // Above threshold (i.e., labelled negative), and should be negative
    return float(FP) / float(FP + TN)
"""import operator


def ErrorRateAt95Recall(labels, scores):

Instances