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| """Provides standard metric evaluations for dialog.""" |
|
|
| from collections import Counter |
| from typing import List |
| import numpy as np |
| import re |
|
|
| re_art = re.compile(r'\b(a|an|the)\b') |
| re_punc = re.compile(r'[!"#$%&()*+,-./:;<=>?@\[\]\\^`{|}~_\']') |
|
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|
|
| def normalize_answer(s): |
| """ |
| Lower text and remove punctuation, articles and extra whitespace. |
| """ |
| s = s.lower() |
| s = re_punc.sub(' ', s) |
| s = re_art.sub(' ', s) |
| s = ' '.join(s.split()) |
| return s |
|
|
|
|
| class F1Metric: |
| """ |
| Helper class which computes token-level F1. |
| """ |
|
|
| @staticmethod |
| def _prec_recall_f1_score(pred_items, gold_items): |
| """ |
| Compute precision, recall and f1 given a set of gold and prediction items. |
| :param pred_items: iterable of predicted values |
| :param gold_items: iterable of gold values |
| :return: tuple (p, r, f1) for precision, recall, f1 |
| """ |
| common = Counter(gold_items) & Counter(pred_items) |
| num_same = sum(common.values()) |
| if num_same == 0: |
| return 0, 0, 0 |
| precision = 1.0 * num_same / len(pred_items) |
| recall = 1.0 * num_same / len(gold_items) |
| f1 = (2 * precision * recall) / (precision + recall) |
| return precision, recall, f1 |
|
|
| @staticmethod |
| def compute_each_pair(guess: str, answer: str): |
| if answer == "": |
| return None, None, None |
| if guess == "": |
| return 0, 0, 0 |
| g_tokens = normalize_answer(guess).split() |
| a_tokens = normalize_answer(answer).split() |
|
|
| precision, recall, f1 = F1Metric._prec_recall_f1_score(g_tokens, a_tokens) |
| return precision, recall, f1 |
| |
| @staticmethod |
| def compute_all_pairs(guesses: List[str], answers: List[list]): |
| assert len(guesses) == len(answers) |
| precision_list, recall_list, f1_list = [], [], [] |
| for guess, answer in zip(guesses, answers): |
| assert type(answer) == list |
| f1_list_tmp = [] |
| for answer_each in answer: |
| answer_each = answer_each.strip() |
| if answer_each == "": |
| continue |
| precision, recall, f1 = F1Metric.compute_each_pair(guess, answer_each) |
| f1_list_tmp.append(f1) |
| |
| if len(f1_list_tmp) > 0: |
| f1 = max(f1_list_tmp) |
| if precision is None or recall is None or f1 is None: |
| continue |
| precision_list.append(precision) |
| recall_list.append(recall) |
| f1_list.append(f1) |
| |
| return np.mean(precision_list), np.mean(recall_list), np.mean(f1_list) |
|
|
