-
Notifications
You must be signed in to change notification settings - Fork 388
/
bleu.py
171 lines (138 loc) · 6.28 KB
/
bleu.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# referenced from
# Library Name: torchtext
# Authors: torchtext authors and @sluks
# Date: 2020-07-18
# Link: https://pytorch.org/text/_modules/torchtext/data/metrics.html#bleu_score
from collections import Counter
from typing import Sequence, Tuple
import torch
from torch import Tensor, tensor
def _count_ngram(ngram_input_list: Sequence[str], n_gram: int) -> Counter:
"""Counting how many times each word appears in a given text with ngram.
Args:
ngram_input_list: A list of translated text or reference texts
n_gram: gram value ranged 1 to 4
Return:
ngram_counter: a collections.Counter object of ngram
"""
ngram_counter: Counter = Counter()
for i in range(1, n_gram + 1):
for j in range(len(ngram_input_list) - i + 1):
ngram_key = tuple(ngram_input_list[j : (i + j)])
ngram_counter[ngram_key] += 1
return ngram_counter
def _bleu_score_update(
reference_corpus: Sequence[Sequence[Sequence[str]]],
translate_corpus: Sequence[Sequence[str]],
numerator: Tensor,
denominator: Tensor,
trans_len: Tensor,
ref_len: Tensor,
n_gram: int = 4,
) -> Tuple[Tensor, Tensor]:
"""Updates and returns variables required to compute the BLEU score.
Args:
reference_corpus: An iterable of iterables of reference corpus
translate_corpus: An iterable of machine translated corpus
numerator: Numerator of precision score (true positives)
denominator: Denominator of precision score (true positives + false positives)
trans_len: count of words in a candidate translation
ref_len: count of words in a reference translation
n_gram: gram value ranged 1 to 4
"""
for (translation, references) in zip(translate_corpus, reference_corpus):
trans_len += len(translation)
ref_len_list = [len(ref) for ref in references]
ref_len_diff = [abs(len(translation) - x) for x in ref_len_list]
ref_len += ref_len_list[ref_len_diff.index(min(ref_len_diff))]
translation_counter: Counter = _count_ngram(translation, n_gram)
reference_counter: Counter = Counter()
for ref in references:
reference_counter |= _count_ngram(ref, n_gram)
ngram_counter_clip = translation_counter & reference_counter
for counter_clip in ngram_counter_clip:
numerator[len(counter_clip) - 1] += ngram_counter_clip[counter_clip]
for counter in translation_counter:
denominator[len(counter) - 1] += translation_counter[counter]
return trans_len, ref_len
def _bleu_score_compute(
trans_len: Tensor, ref_len: Tensor, numerator: Tensor, denominator: Tensor, n_gram: int = 4, smooth: bool = False
) -> Tensor:
"""Computes the BLEU score.
Args:
trans_len: count of words in a candidate translation
ref_len: count of words in a reference translation
numerator: Numerator of precision score (true positives)
denominator: Denominator of precision score (true positives + false positives)
n_gram: gram value ranged 1 to 4
smooth: Whether or not to apply smoothing
"""
device = numerator.device
if min(numerator) == 0.0:
return tensor(0.0, device=device)
if smooth:
precision_scores = torch.div(
torch.add(numerator, torch.ones(n_gram, device=device)),
torch.add(denominator, torch.ones(n_gram, device=device)),
)
precision_scores[0] = numerator[0] / denominator[0]
else:
precision_scores = numerator / denominator
log_precision_scores = tensor([1.0 / n_gram] * n_gram, device=device) * torch.log(precision_scores)
geometric_mean = torch.exp(torch.sum(log_precision_scores))
brevity_penalty = tensor(1.0, device=device) if trans_len > ref_len else torch.exp(1 - (ref_len / trans_len))
bleu = brevity_penalty * geometric_mean
return bleu
def bleu_score(
reference_corpus: Sequence[Sequence[Sequence[str]]],
translate_corpus: Sequence[Sequence[str]],
n_gram: int = 4,
smooth: bool = False,
) -> Tensor:
"""Calculate `BLEU score`_ of machine translated text with one or more references.
Args:
reference_corpus:
An iterable of iterables of reference corpus
translate_corpus:
An iterable of machine translated corpus
n_gram:
Gram value ranged from 1 to 4 (Default 4)
smooth:
Whether or not to apply smoothing – see [2]
Return:
Tensor with BLEU Score
Example:
>>> from torchmetrics.functional import bleu_score
>>> translate_corpus = ['the cat is on the mat'.split()]
>>> reference_corpus = [['there is a cat on the mat'.split(), 'a cat is on the mat'.split()]]
>>> bleu_score(reference_corpus, translate_corpus)
tensor(0.7598)
References:
[1] BLEU: a Method for Automatic Evaluation of Machine Translation by Papineni,
Kishore, Salim Roukos, Todd Ward, and Wei-Jing Zhu `BLEU`_
[2] Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence
and Skip-Bigram Statistics by Chin-Yew Lin and Franz Josef Och `Machine Translation Evolution`_
"""
if len(translate_corpus) != len(reference_corpus):
raise ValueError(f"Corpus has different size {len(translate_corpus)} != {len(reference_corpus)}")
numerator = torch.zeros(n_gram)
denominator = torch.zeros(n_gram)
trans_len = tensor(0, dtype=torch.float)
ref_len = tensor(0, dtype=torch.float)
trans_len, ref_len = _bleu_score_update(
reference_corpus, translate_corpus, numerator, denominator, trans_len, ref_len, n_gram
)
return _bleu_score_compute(trans_len, ref_len, numerator, denominator, n_gram, smooth)