86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
"""
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These are the official evaluation metrics for HotpotQA taken from https://hotpotqa.github.io/
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"""
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import re
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import string
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import sys
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from collections import Counter
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import ujson as json
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def normalize_answer(s):
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def remove_articles(text):
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return re.sub(r'\b(a|an|the)\b', ' ', text)
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def white_space_fix(text):
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return ' '.join(text.split())
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def remove_punc(text):
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exclude = set(string.punctuation)
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return ''.join(ch for ch in text if ch not in exclude)
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def lower(text):
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return text.lower()
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return white_space_fix(remove_articles(remove_punc(lower(s))))
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def f1_score(prediction, ground_truth):
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normalized_prediction = normalize_answer(prediction)
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normalized_ground_truth = normalize_answer(ground_truth)
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ZERO_METRIC = (0, 0, 0)
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if normalized_prediction in ['yes', 'no', 'noanswer'] and normalized_prediction != normalized_ground_truth:
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return ZERO_METRIC
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if normalized_ground_truth in ['yes', 'no', 'noanswer'] and normalized_prediction != normalized_ground_truth:
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return ZERO_METRIC
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prediction_tokens = normalized_prediction.split()
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ground_truth_tokens = normalized_ground_truth.split()
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common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
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num_same = sum(common.values())
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if num_same == 0:
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return ZERO_METRIC
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precision = 1.0 * num_same / len(prediction_tokens)
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recall = 1.0 * num_same / len(ground_truth_tokens)
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f1 = (2 * precision * recall) / (precision + recall)
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return f1, precision, recall
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def exact_match_score(prediction, ground_truth):
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return (normalize_answer(prediction) == normalize_answer(ground_truth))
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def update_answer(metrics, prediction, gold):
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em = exact_match_score(prediction, gold)
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f1, prec, recall = f1_score(prediction, gold)
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metrics['em'] += float(em)
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metrics['f1'] += f1
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metrics['prec'] += prec
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metrics['recall'] += recall
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return em, prec, recall
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def update_sp(metrics, prediction, gold):
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cur_sp_pred = set(map(tuple, prediction))
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gold_sp_pred = set(map(tuple, gold))
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tp, fp, fn = 0, 0, 0
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for e in cur_sp_pred:
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if e in gold_sp_pred:
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tp += 1
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else:
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fp += 1
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for e in gold_sp_pred:
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if e not in cur_sp_pred:
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fn += 1
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prec = 1.0 * tp / (tp + fp) if tp + fp > 0 else 0.0
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recall = 1.0 * tp / (tp + fn) if tp + fn > 0 else 0.0
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f1 = 2 * prec * recall / (prec + recall) if prec + recall > 0 else 0.0
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em = 1.0 if fp + fn == 0 else 0.0
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metrics['sp_em'] += em
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metrics['sp_f1'] += f1
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metrics['sp_prec'] += prec
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metrics['sp_recall'] += recall
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return em, prec, recall
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