观看了Andrew Ng关于Bleu score的视频后，我想从头开始用python实现一个视频。我很少用numpy用python编写完整的代码。这是完整的代码
import numpy as np
def n_gram_generator(sentence,n= 2,n_gram= False):
'''
N-Gram generator with parameters sentence
n is for number of n_grams
The n_gram parameter removes repeating n_grams
'''
sentence = sentence.lower() # converting to lower case
sent_arr = np.array(sentence.split()) # split to string arrays
length = len(sent_arr)
word_list = []
for i in range(length+1):
if i < n:
continue
word_range = list(range(i-n,i))
s_list = sent_arr[word_range]
string = ' '.join(s_list) # converting list to strings
word_list.append(string) # append to word_list
if n_gram:
word_list = list(set(word_list))
return word_list
def bleu_score(original,machine_translated):
'''
Bleu score function given a orginal and a machine translated sentences
'''
mt_length = len(machine_translated.split())
o_length = len(original.split())
# Brevity Penalty
if mt_length>o_length:
BP=1
else:
penality=1-(mt_length/o_length)
BP=np.exp(penality)
# calculating precision
precision_score = []
for i in range(mt_length):
original_n_gram = n_gram_generator(original,i)
machine_n_gram = n_gram_generator(machine_translated,i)
n_gram_list = list(set(machine_n_gram)) # removes repeating strings
# counting number of occurence
machine_score = 0
original_score = 0
for j in n_gram_list:
machine_count = machine_n_gram.count(j)
original_count = original_n_gram.count(j)
machine_score = machine_score+machine_count
original_score = original_score+original_count
precision = original_score/machine_score
precision_score.append(precision)
precisions_sum = np.array(precision_score).sum()
avg_precisions_sum=precisions_sum/mt_length
bleu=BP*np.exp(avg_precisions_sum)
return bleu
if __name__ == "__main__":
original = "this is a test"
bs=bleu_score(original,original)
print("Bleu Score Original",bs)
我试图用nltk的
来测试我的分数from nltk.translate.bleu_score import sentence_bleu
reference = [['this', 'is', 'a', 'test']]
candidate = ['this', 'is', 'a', 'test']
score = sentence_bleu(reference, candidate)
print(score)
问题在于我的bleu得分约为2.718281，nltk的得分为1。我究竟做错了什么？
以下是一些可能的原因：
1）我根据机器翻译句子的长度计算了ngram。从1到4
2）我自己写的n_gram_generator函数，不确定其准确性
3）一些我使用错误的功能或错误计算的青斑分数的方法
有人可以查一下我的代码并告诉我我在哪里做错了吗？
答案 0 :(得分：2)
您的Bleu分数计算错误。 问题：
更正的代码
def bleu_score(original,machine_translated):
'''
Bleu score function given a orginal and a machine translated sentences
'''
mt_length = len(machine_translated.split())
o_length = len(original.split())
# Brevity Penalty
if mt_length>o_length:
BP=1
else:
penality=1-(mt_length/o_length)
BP=np.exp(penality)
# Clipped precision
clipped_precision_score = []
for i in range(1, 5):
original_n_gram = Counter(n_gram_generator(original,i))
machine_n_gram = Counter(n_gram_generator(machine_translated,i))
c = sum(machine_n_gram.values())
for j in machine_n_gram:
if j in original_n_gram:
if machine_n_gram[j] > original_n_gram[j]:
machine_n_gram[j] = original_n_gram[j]
else:
machine_n_gram[j] = 0
#print (sum(machine_n_gram.values()), c)
clipped_precision_score.append(sum(machine_n_gram.values())/c)
#print (clipped_precision_score)
weights =[0.25]*4
s = (w_i * math.log(p_i) for w_i, p_i in zip(weights, clipped_precision_score))
s = BP * math.exp(math.fsum(s))
return s
original = "It is a guide to action which ensures that the military alwasy obeys the command of the party"
machine_translated = "It is the guiding principle which guarantees the military forces alwasy being under the command of the party"
print (bleu_score(original, machine_translated))
print (sentence_bleu([original.split()], machine_translated.split()))
输出：
0.27098211583470044
0.27098211583470044
答案 1 :(得分：1)
这里是实际nltk source code的略微修改版本：
def sentence_bleu_man(
references,
hypothesis,
weights=(0.25, 0.25, 0.25, 0.25)):
# compute modified precision for 1-4 ngrams
p_numerators = Counter()
p_denominators = Counter()
hyp_lengths, ref_lengths = 0, 0
for i, _ in enumerate(weights, start=1):
p_i = modified_precision(references, hypothesis, i)
p_numerators[i] += p_i.numerator
p_denominators[i] += p_i.denominator
# compute brevity penalty
hyp_len = len(hypothesis)
ref_len = closest_ref_length(references, hyp_len)
bp = brevity_penalty(ref_len, hyp_len)
# compute final score
p_n = [
Fraction(p_numerators[i], p_denominators[i],
_normalize=False)
for i, _ in enumerate(weights, start=1)
if p_numerators[i] > 0
]
s = (w_i * math.log(p_i) for w_i, p_i in zip(weights, p_n))
s = bp * math.exp(math.fsum(s))
return s
我们可以使用原始paper中的示例：
rt_raw = [
'It is a guide to action that ensures that the military will forever heed Party commands',
'It is the guiding principle which guarantees the military forces always being under the command of the Party',
'It is the practical guide for the army always to heed the directions of the party'
]
ct_raw = [
'It is a guide to action which ensures that the military always obeys the commands of the party',
'It is to insure the troops forever hearing the activity guidebook that party direct'
]
def process_trans(t):
return t.lower().split()
rt = [process_trans(t) for t in rt_raw]
ct = [process_trans(t) for t in ct_raw]
c1, c2 = ct[0], ct[1]
sentence_bleu_man(rt, c2, weights=(.5, .5, 0, 0))
sentence_bleu(rt, c2, weights=(.5, .5, 0, 0))
输出：
0.18174699151949172
0.18174699151949172
答案 2 :(得分：1)
此处是修订的解决方案
# coding: utf-8
import numpy as np
from collections import Counter
import math
from nltk.translate.bleu_score import sentence_bleu
def n_gram_generator(sentence,n= 2,n_gram= False):
'''
N-Gram generator with parameters sentence
n is for number of n_grams
The n_gram parameter removes repeating n_grams
'''
sentence = sentence.lower() # converting to lower case
sent_arr = np.array(sentence.split()) # split to string arrays
length = len(sent_arr)
word_list = []
for i in range(length+1):
if i < n:
continue
word_range = list(range(i-n,i))
s_list = sent_arr[word_range]
string = ' '.join(s_list) # converting list to strings
word_list.append(string) # append to word_list
if n_gram:
word_list = list(set(word_list))
return word_list
def bleu_score(original, machine_translated):
'''
Bleu score function given a orginal and a machine translated sentences
'''
mt_length = len(machine_translated.split())
o_length = len(original.split())
# Brevity Penalty
if mt_length > o_length:
BP=1
else:
penality=1-(mt_length/o_length)
BP = np.exp(penality)
# Clipped precision
clipped_precision_score = []
for ngram_level in range(1, 5): # 1-gram to 4-gram
original_ngram_list = n_gram_generator(original, ngram_level)
original_n_gram = Counter(original_ngram_list)
machine_ngram_list = n_gram_generator(machine_translated, ngram_level)
machine_n_gram = Counter(machine_ngram_list)
num_ngrams_in_translation = sum(machine_n_gram.values()) # number of ngrams in translation
# iterate the unique ngrams in translation (candidate)
for j in machine_n_gram:
if j in original_n_gram: # if found in reference
if machine_n_gram[j] > original_n_gram[j]: # CLIPPING - if found in translation more than in source, clip
machine_n_gram[j] = original_n_gram[j]
else:
machine_n_gram[j] = 0
#print (sum(machine_n_gram.values()), c)
clipped_precision_score.append(float(sum(machine_n_gram.values())) / num_ngrams_in_translation)
#print (clipped_precision_score)
weights = [0.25]*4
s = (w_i * math.log(p_i) for w_i, p_i in zip(weights, clipped_precision_score))
s = BP * math.exp(math.fsum(s))
return s
original = "It is a guide to action which ensures that the military alwasy obeys the command of the party"
machine_translated = "It is the guiding principle which guarantees the military forces alwasy being under the command of the party"
print (bleu_score(original, machine_translated))
print (sentence_bleu([original.split()], machine_translated.split()))