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# 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)
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