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hotpot_evaluate_v1.py
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hotpot_evaluate_v1.py
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import sys
import ujson as json
import re
import string
from collections import Counter
import pickle
from IPython import embed
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'] += em
metrics['f1'] += f1
metrics['prec'] += prec
metrics['recall'] += recall
return em, prec, recall
def update_sp(metrics, prediction, gold):
cur_sp_pred = set(map(tuple, prediction))
gold_sp_pred = 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 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,
'joint_em': 0, 'joint_f1': 0, 'joint_prec': 0, 'joint_recall': 0}
for dp in gold:
cur_id = dp['_id']
em, prec, recall = update_answer(
metrics, prediction['answer'][cur_id], dp['answer'])
N = len(gold)
for k in metrics.keys():
metrics[k] /= N
print(metrics)
def analyze(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,
'joint_em': 0, 'joint_f1': 0, 'joint_prec': 0, 'joint_recall': 0}
for dp in gold:
cur_id = dp['_id']
em, prec, recall = update_answer(
metrics, prediction['answer'][cur_id], dp['answer'])
if (prec + recall == 0):
f1 = 0
else:
f1 = 2 * prec * recall / (prec+recall)
print (dp['answer'], prediction['answer'][cur_id])
print (f1, em)
a = input()
if __name__ == '__main__':
#eval(sys.argv[1], sys.argv[2])
analyze(sys.argv[1], sys.argv[2])