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| """ Official evaluation script for v1.1 of the SQuAD dataset. """ | |
| import argparse | |
| import json | |
| import re | |
| import string | |
| import sys | |
| from collections import Counter | |
| 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 recall_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 | |
| recall = 1.0 * num_same / len(ground_truth_tokens) | |
| return recall | |
| def precision_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) | |
| return precision | |
| # 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 compute_score(dataset, predictions): | |
| recall = precision = 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) | |
| recall_temp = metric_max_over_ground_truths(recall_score, prediction, ground_truths) | |
| precision_temp = metric_max_over_ground_truths(precision_score, prediction, ground_truths) | |
| if recall_temp + precision_temp == 0: | |
| f1_temp = 0 | |
| else: | |
| f1_temp = (2 * precision_temp * recall_temp) / (precision_temp + recall_temp) | |
| f1 += f1_temp | |
| recall += recall_temp | |
| precision += precision_temp | |
| exact_match = 100.0 * exact_match / total | |
| f1 = 100.0 * f1 / total | |
| precision = 100.0 * precision / total | |
| recall = 100.0 * recall / total | |
| return {"exact_match": exact_match, "f1": f1, "recall": recall, "precision": precision} | |
| 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(compute_score(dataset, predictions))) | |