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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 |
| |
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| |
|
| | 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) |
| |
|
