utils_prompt_tuning.py

"""Official evaluation script for the MRQA Workshop Shared Task.
Adapted fromt the SQuAD v1.1 official evaluation script.
Usage:
    python official_eval.py dataset_file.jsonl.gz prediction_file.json
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import string
import re
import json
import gzip
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 read_predictions(prediction_file):
    with open(prediction_file) as f:
        predictions = json.load(f)
    return predictions


def read_answers(gold_file):
    answers = {}
    with gzip.open(gold_file, 'rb') as f:
        for i, line in enumerate(f):
            example = json.loads(line)
            if i == 0 and 'header' in example:
                continue
            for qa in example['qas']:
                answers[qa['qid']] = qa['answers']
    return answers


def evaluate(answers, predictions, skip_no_answer=False):
    f1 = exact_match = total = 0
    for qid, ground_truths in answers.items():
        if qid not in predictions:
            if not skip_no_answer:
                message = 'Unanswered question %s will receive score 0.' % qid
                print(message)
                total += 1
            continue
        total += 1
        prediction = predictions[qid]
        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}