# The following code is adapted from # https://github.com/facebookresearch/ParlAI/blob/master/parlai/core/metrics.py, # which is licensed under the MIT license. More details on the license can be # found at https://github.com/facebookresearch/ParlAI/blob/master/LICENSE. """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'[!"#$%&()*+,-./:;<=>?@\[\]\\^`{|}~_\']') 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)