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| # coding=utf-8 | |
| # Based on the SQuAD evaluation script from: | |
| # https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py | |
| """ Official evaluation script for v1.1 of the SQuAD dataset. """ | |
| from __future__ import print_function | |
| from collections import Counter | |
| import string | |
| import re | |
| import argparse | |
| import json | |
| import sys | |
| 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 {'exact_match': exact_match, 'f1': f1} | |
| def evaluate_with_path(dataset_file, prediction_file): | |
| with open(dataset_file) as dataset_file_reader: | |
| dataset_json = json.load(dataset_file_reader) | |
| dataset = dataset_json['data'] | |
| with open(prediction_file) as prediction_file_reader: | |
| predictions = json.load(prediction_file_reader) | |
| return evaluate(dataset, predictions) | |
| 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(json.dumps(evaluate(dataset, predictions))) |