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f1_thresh_evals.py
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""" get scores for F1 Threshold(rho) experiments for competitive approaches """
# requires:
# - json file with predicted answers
# - json data file
from __future__ import print_function
from collections import Counter
import string
import re
import argparse
import json
import sys
import numpy as np
from collections import OrderedDict
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
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):
prediction_tokens = normalize_answer(prediction).split()
ground_truth_tokens = normalize_answer(ground_truth).split()
common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
num_same = sum(common.values())
if num_same == 0:
return 0
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
def exact_match_score(prediction, ground_truth):
return (normalize_answer(prediction) == normalize_answer(ground_truth))
def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
scores_for_ground_truths = []
for ground_truth in ground_truths:
score = metric_fn(prediction, ground_truth)
scores_for_ground_truths.append(score)
return max(scores_for_ground_truths)
def evaluate(dataset, predictions):
f1 = exact_match = total = 0
for article in dataset:
for paragraph in article['paragraphs']:
for qa in paragraph['qas']:
total += 1
if qa['id'] not in predictions:
message = 'Unanswered question ' + qa['id'] + \
' will receive score 0.'
print(message, file=sys.stderr)
continue
ground_truths = list(map(lambda x: x['text'], qa['answers']))
prediction = predictions[qa['id']]
exact_match += metric_max_over_ground_truths(
exact_match_score, prediction, ground_truths)
f1 += metric_max_over_ground_truths(
f1_score, prediction, ground_truths)
exact_match = 100.0 * exact_match / total
f1 = 100.0 * f1 / total
return {'EM': round(exact_match, 2), 'F1': round(f1, 2)}
def evaluate_dev_flat(dataset, predictions):
f1 = exact_match = total = 0
for article in dataset:
for paragraph in article['paragraphs']:
for qa in paragraph['qas']:
if qa['id'] not in predictions:
message = 'Unanswered question ' + qa['id'] + \
' will receive score 0.'
print(message, file=sys.stderr)
continue
ground_truths = list(map(lambda x: x['text'], qa['answers']))
ans_set = set(ground_truths)
for a_i in range(len(set(ground_truths))):
if qa['answers'][a_i]['text'] in ans_set:
ans_set.remove(qa['answers'][a_i]['text'])
total += 1
else:
continue
gt_ans = [qa['answers'][a_i]['text']]
prediction = predictions[qa['id']]
exact_match += metric_max_over_ground_truths(
exact_match_score, prediction, gt_ans)
f1 += metric_max_over_ground_truths(
f1_score, prediction, gt_ans)
exact_match = 100.0 * exact_match / total
f1 = 100.0 * f1 / total
return {'EM': round(exact_match, 2), 'F1': round(f1, 2)}
def toscore(score, total):
d = {}
for p,s in score.items():
d[p] = round(100.*s/total, 2)
td = OrderedDict(sorted(d.items(), key=lambda t: t[0]))
return td
def evaluate_dev_a(dataset, predictions, n_ans):
f1 = exact_match = total = 0
t_a = {0.1:0, 0.2:0, 0.3:0, 0.4:0, 0.5:0, 0.6:0, 0.7:0, 0.8:0, 0.9:0}
f1_all = []
for article in dataset:
for paragraph in article['paragraphs']:
for qa in paragraph['qas']:
if qa['id'] not in predictions:
message = 'Unanswered question ' + qa['id'] + \
' will receive score 0.'
print(message, file=sys.stderr)
continue
ground_truths = list(map(lambda x: x['text'], qa['answers']))
ans_set = set(ground_truths)
if len(ans_set) < n_ans or (n_ans == 1 and len(ans_set)>1):
continue
ans_set = list(ans_set)[:n_ans]
f1s = []; total += 1
prediction = predictions[qa['id']]
for a in ans_set:
f1s += [metric_max_over_ground_truths(f1_score, prediction, [a])]
f1_all += [max(f1s)]
f1s = np.array(f1s)
for p in t_a.keys():
t_a[p] = t_a[p] + int((f1s>p).sum() == n_ans)
f1s_all = round(100. * sum(f1_all) / len(f1_all), 2)
t_ans = toscore(t_a, total)
return t_ans, total, f1s_all
if __name__ == '__main__':
expected_version = '1.1'
parser = argparse.ArgumentParser(
description='Evaluation for SQuAD ' + expected_version)
parser.add_argument('dataset_file', help='Dataset file')
parser.add_argument('prediction_file', help='Prediction File')
args = parser.parse_args()
with open(args.dataset_file) as dataset_file:
dataset_json = json.load(dataset_file)
if (dataset_json['version'] != expected_version):
print('Evaluation expects v-' + expected_version +
', but got dataset with v-' + dataset_json['version'],
file=sys.stderr)
dataset = dataset_json['data']
with open(args.prediction_file) as prediction_file:
predictions = json.load(prediction_file)
print("Orig dev set", json.dumps(evaluate(dataset, predictions)))
print("Flat dev set", json.dumps(evaluate_dev_flat(dataset, predictions)))
print("Dev |a|=1", json.dumps(evaluate_dev_a(dataset, predictions, 1)))
print("Dev |a|=2", json.dumps(evaluate_dev_a(dataset, predictions, 2)))
print("Dev |a|=3", json.dumps(evaluate_dev_a(dataset, predictions, 3)))