record_evaluation.py

"""
Official evaluation script for ReCoRD v1.0.
(Some functions are adopted from the SQuAD evaluation script.)
"""


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 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
    correct_ids = []
    for passage in dataset:
        for qa in passage["qas"]:
            total += 1
            if qa["id"] not in predictions:
                message = f'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)
            if int(_exact_match) == 1:
                correct_ids.append(qa["id"])
            exact_match += _exact_match

            f1 += metric_max_over_ground_truths(f1_score, prediction, ground_truths)

    exact_match = exact_match / total
    f1 = f1 / total

    return {"exact_match": exact_match, "f1": f1}, correct_ids


if __name__ == "__main__":
    expected_version = "1.0"
    parser = argparse.ArgumentParser("Official evaluation script for ReCoRD v1.0.")
    parser.add_argument("data_file", help="The dataset file in JSON format.")
    parser.add_argument("pred_file", help="The model prediction file in JSON format.")
    parser.add_argument("--output_correct_ids", action="store_true", help="Output the correctly answered query IDs.")
    args = parser.parse_args()

    with open(args.data_file) as data_file:
        dataset_json = json.load(data_file)
        if dataset_json["version"] != expected_version:
            print(
                f'Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json["version"]}',
                file=sys.stderr,
            )
        dataset = dataset_json["data"]

    with open(args.pred_file) as pred_file:
        predictions = json.load(pred_file)

    metrics, correct_ids = evaluate(dataset, predictions)

    if args.output_correct_ids:
        print(f"Output {len(correct_ids)} correctly answered question IDs.")
        with open("correct_ids.json", "w") as f:
            json.dump(correct_ids, f)