count = 0
for label, score in sorted_scores:
count += 1
if label == 1:
tp += 1
if tp >= n_thresh:
break
return float(count - tp) / count
After Change
def ErrorRateAt95Recall(labels, scores):
distances = 1.0 / (scores + 1e-8)
recall_point = 0.95
labels = labels[np.argsort(distances)]
// Sliding threshold: get first index where recall >= recall_point.
// This is the index where the number of elements with label==1 below the threshold reaches a fraction of
// "recall_point" of the total number of elements with label==1.
// (np.argmax returns the first occurrence of a "1" in a bool array).
threshold_index = np.argmax(np.cumsum(labels) >= recall_point * np.sum(labels))
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
