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official_evaluation.py
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official_evaluation.py
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"""
2Wiki-Multihop QA evaluation script
Adapted from HotpotQA evaluation at https://github.com/hotpotqa/hotpot
"""
import sys
import ujson as json
import re
import string
from collections import Counter
import pickle
def normalize_answer(s):
def remove_articles(text):
return re.sub(r'\b(a|an|the)\b', ' ', text)
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return ''.join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def f1_score(prediction, ground_truth):
normalized_prediction = normalize_answer(prediction)
normalized_ground_truth = normalize_answer(ground_truth)
ZERO_METRIC = (0, 0, 0)
if normalized_prediction in ['yes', 'no', 'noanswer'] and normalized_prediction != normalized_ground_truth:
return ZERO_METRIC
if normalized_ground_truth in ['yes', 'no', 'noanswer'] and normalized_prediction != normalized_ground_truth:
return ZERO_METRIC
prediction_tokens = normalized_prediction.split()
ground_truth_tokens = normalized_ground_truth.split()
common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
num_same = sum(common.values())
if num_same == 0:
return ZERO_METRIC
precision = 1.0 * num_same / len(prediction_tokens)
recall = 1.0 * num_same / len(ground_truth_tokens)
f1 = (2 * precision * recall) / (precision + recall)
return f1, precision, recall
def exact_match_score(prediction, ground_truth):
return (normalize_answer(prediction) == normalize_answer(ground_truth))
def update_answer(metrics, prediction, gold):
em = exact_match_score(prediction, gold)
f1, prec, recall = f1_score(prediction, gold)
metrics['em'] += float(em)
metrics['f1'] += f1
metrics['prec'] += prec
metrics['recall'] += recall
return em, prec, recall
def normalize_sp(sps):
new_sps = []
for sp in sps:
sp = list(sp)
sp[0] = sp[0].lower()
new_sps.append(sp)
return new_sps
def update_sp(metrics, prediction, gold):
cur_sp_pred = normalize_sp(set(map(tuple, prediction)))
gold_sp_pred = normalize_sp(set(map(tuple, gold)))
tp, fp, fn = 0, 0, 0
for e in cur_sp_pred:
if e in gold_sp_pred:
tp += 1
else:
fp += 1
for e in gold_sp_pred:
if e not in cur_sp_pred:
fn += 1
prec = 1.0 * tp / (tp + fp) if tp + fp > 0 else 0.0
recall = 1.0 * tp / (tp + fn) if tp + fn > 0 else 0.0
f1 = 2 * prec * recall / (prec + recall) if prec + recall > 0 else 0.0
em = 1.0 if fp + fn == 0 else 0.0
metrics['sp_em'] += em
metrics['sp_f1'] += f1
metrics['sp_prec'] += prec
metrics['sp_recall'] += recall
return em, prec, recall
def normalize_evi(evidences):
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return ''.join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
for idx_1 in range(len(evidences)):
for idx_2 in range(len(evidences[idx_1])):
evidences[idx_1][idx_2] = white_space_fix((remove_punc(lower(evidences[idx_1][idx_2]))))
return evidences
def update_evi(metrics, prediction, gold):
prediction_normalize = normalize_evi(prediction)
gold_normalize = normalize_evi(gold)
#
cur_evi_pred = set(map(tuple, prediction_normalize))
gold_evi_pred = set(map(tuple, gold_normalize))
#
#
tp, fp, fn = 0, 0, 0
for e in cur_evi_pred:
if e in gold_evi_pred:
tp += 1
else:
fp += 1
for e in gold_evi_pred:
if e not in cur_evi_pred:
fn += 1
prec = 1.0 * tp / (tp + fp) if tp + fp > 0 else 0.0
recall = 1.0 * tp / (tp + fn) if tp + fn > 0 else 0.0
f1 = 2 * prec * recall / (prec + recall) if prec + recall > 0 else 0.0
em = 1.0 if fp + fn == 0 else 0.0
metrics['evi_em'] += em
metrics['evi_f1'] += f1
metrics['evi_prec'] += prec
metrics['evi_recall'] += recall
return em, prec, recall
def eval(prediction_file, gold_file):
with open(prediction_file) as f:
prediction = json.load(f)
with open(gold_file) as f:
gold = json.load(f)
metrics = {'em': 0, 'f1': 0, 'prec': 0, 'recall': 0,
'sp_em': 0, 'sp_f1': 0, 'sp_prec': 0, 'sp_recall': 0,
'evi_em': 0, 'evi_f1': 0, 'evi_prec': 0, 'evi_recall': 0,
'joint_em': 0, 'joint_f1': 0, 'joint_prec': 0, 'joint_recall': 0}
for dp in gold:
cur_id = dp['_id']
can_eval_joint = True
if cur_id not in prediction['answer']:
print('missing answer {}'.format(cur_id))
can_eval_joint = False
else:
em, prec, recall = update_answer(
metrics, prediction['answer'][cur_id], dp['answer'])
if cur_id not in prediction['sp']:
print('missing sp fact {}'.format(cur_id))
can_eval_joint = False
else:
sp_em, sp_prec, sp_recall = update_sp(
metrics, prediction['sp'][cur_id], dp['supporting_facts'])
#
if cur_id not in prediction['evidence']:
print('missing evidence {}'.format(cur_id))
can_eval_joint = False
else:
evi_em, evi_prec, evi_recall = update_evi(
metrics, prediction['evidence'][cur_id], dp['evidences'])
if can_eval_joint:
joint_prec = prec * sp_prec * evi_prec
joint_recall = recall * sp_recall * evi_recall
#
if joint_prec + joint_recall > 0:
joint_f1 = 2 * joint_prec * joint_recall / (joint_prec + joint_recall)
else:
joint_f1 = 0.
joint_em = em * sp_em * evi_em
metrics['joint_em'] += joint_em
metrics['joint_f1'] += joint_f1
metrics['joint_prec'] += joint_prec
metrics['joint_recall'] += joint_recall
N = len(gold)
for k in metrics.keys():
metrics[k] = round(metrics[k]/N*100, 2)
print(json.dumps(metrics, indent=4))
if __name__ == '__main__':
eval(sys.argv[1], sys.argv[2])