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max_em = 0
max_f1 = 0
for gold_answer in golds:
(exact_match, f1_score) = get_metrics(preds, gold_answer)
if gold_answer[0].strip():
max_em = max(max_em, exact_match)
max_f1 = max(max_f1, f1_score)
return {'em': max_em, 'f1': max_f1}
def get_metrics(predicted, gold):
predicted_bags = _answer_to_bags(predicted)
gold_bags = _answer_to_bags(gold)
if set(predicted_bags[0]) == set(gold_bags[0]) and len(predicted_bags[0]) == len(gold_bags[0]):
exact_match = 1.0
else:
exact_match = 0.0
f1_per_bag = _align_bags(predicted_bags[1], gold_bags[1])
f1 = np.mean(f1_per_bag)
f1 = round(f1, 2)
return (exact_match, f1)
def _answer_to_bags(answer):
if isinstance(answer, (list, tuple)):
raw_spans = answer
else:
raw_spans = [answer]
normalized_spans = []
token_bags = []
for raw_span in raw_spans:
normalized_span = _normalize(raw_span)
normalized_spans.append(normalized_span)
token_bags.append(set(normalized_span.split()))
return (normalized_spans, token_bags)
def _align_bags(predicted, gold):
scores = np.zeros([len(gold), len(predicted)])
for (gold_index, gold_item) in enumerate(gold):
for (pred_index, pred_item) in enumerate(predicted):
if _match_numbers_if_present(gold_item, pred_item):
scores[gold_index, pred_index] = _compute_f1(pred_item, gold_item)
(row_ind, col_ind) = linear_sum_assignment(-scores)
max_scores = np.zeros([max(len(gold), len(predicted))])
for (row, column) in zip(row_ind, col_ind):
max_scores[row] = max(max_scores[row], scores[row, column])
return max_scores
def _compute_f1(predicted_bag, gold_bag):
intersection = len(gold_bag.intersection(predicted_bag))
if not predicted_bag:
precision = 1.0
else:
precision = intersection / float(len(predicted_bag))
if not gold_bag:
recall = 1.0
else:
recall = intersection / float(len(gold_bag))
f1 = 2 * precision * recall / (precision + recall) if not (precision == 0.0 and recall == 0.0) else 0.0
return f1
def _match_numbers_if_present(gold_bag, predicted_bag):
gold_numbers = set()
predicted_numbers = set()
for word in gold_bag:
if _is_number(word):
gold_numbers.add(word)
for word in predicted_bag:
if _is_number(word):
predicted_numbers.add(word)
if not gold_numbers or gold_numbers.intersection(predicted_numbers):
return True
return False
def _is_number(text):
try:
float(text)
return True
except ValueError:
return False
def _remove_articles(text):
return _ARTICLES.sub(' ', text)
def _white_space_fix(text):
return ' '.join(text.split())
def _remove_punc(text):
exclude = set(string.punctuation)
if not _is_number(text):
return ''.join((ch for ch in text if ch not in exclude))
else:
return text
def _fix_number(text):
return str(float(text)) if _is_number(text) else text
def _tokenize(text):
return re.split(' |-', text)
def _normalize(answer):
tokens = [_white_space_fix(_remove_articles(_fix_number(_remove_punc(token.lower())))) for token in _tokenize(answer)]