utils.py

import re
import string

from collections import Counter


def normalize_answer(s):

    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):
    normalized_prediction = normalize_answer(prediction)
    normalized_ground_truth = normalize_answer(ground_truth)

    ZERO_METRIC = (0, 0, 0)

    if normalized_prediction in ['yes', 'no', 'noanswer'] and normalized_prediction != normalized_ground_truth:
        return ZERO_METRIC
    if normalized_ground_truth in ['yes', 'no', 'noanswer'] and normalized_prediction != normalized_ground_truth:
        return ZERO_METRIC

    prediction_tokens = normalized_prediction.split()
    ground_truth_tokens = normalized_ground_truth.split()
    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
    num_same = sum(common.values())
    if num_same == 0:
        return ZERO_METRIC
    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, precision, recall


def exact_match_score(prediction, ground_truth):
    return (normalize_answer(prediction) == normalize_answer(ground_truth))


def drqa_normalize(text):
    """Resolve different type of unicode encodings."""
    return unicodedata.normalize('NFD', text)


def drqa_exact_match_score(prediction, ground_truth):
    """Check if the prediction is a (soft) exact match with the ground truth."""
    return normalize_answer(prediction) == normalize_answer(ground_truth)


def drqa_metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
    """Given a prediction and multiple valid answers, return the score of
    the best prediction-answer_n pair given a metric function.
    """
    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)