Created
December 26, 2023 08:59
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some simple python utilities copied from other sources
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""" | |
Copied from sentence-transformers / sentence_transformers/evaluation/BinaryClassificationEvaluator.py | |
""" | |
def find_best_f1_and_threshold(scores, labels, high_score_more_similar: bool): | |
assert len(scores) == len(labels) | |
scores = np.asarray(scores) | |
labels = np.asarray(labels) | |
rows = list(zip(scores, labels)) | |
rows = sorted(rows, key=lambda x: x[0], reverse=high_score_more_similar) | |
best_f1 = best_precision = best_recall = 0 | |
threshold = 0 | |
nextract = 0 | |
ncorrect = 0 | |
total_num_duplicates = sum(labels) | |
for i in range(len(rows)-1): | |
score, label = rows[i] | |
nextract += 1 | |
if label == 1: | |
ncorrect += 1 | |
if ncorrect > 0: | |
precision = ncorrect / nextract | |
recall = ncorrect / total_num_duplicates | |
f1 = 2 * precision * recall / (precision + recall) | |
if f1 > best_f1: | |
best_f1 = f1 | |
best_precision = precision | |
best_recall = recall | |
threshold = (rows[i][0] + rows[i + 1][0]) / 2 | |
return best_f1, best_precision, best_recall, threshold |
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