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import os
from multiprocessing import Pool
import pdb
import numpy as np
import nltk
nltk.download('punkt')
from nltk.translate.bleu_score import SmoothingFunction
try:
from multiprocessing import cpu_count
except:
from os import cpu_count
class Metrics(object):
def __init__(self):
self.name = 'Metric'
def get_name(self):
return self.name
def set_name(self, name):
self.name = name
def get_score(self):
pass
class Bleu(Metrics):
def __init__(self, test_text='', real_text='', gram=3, num_real_sentences=500, num_fake_sentences=10000):
super(Bleu, self).__init__()
self.name = 'Bleu'
self.test_data = test_text
self.real_data = real_text
self.gram = gram
self.sample_size = num_real_sentences
self.reference = None
self.is_first = True
self.num_sentences = num_fake_sentences
def get_name(self):
return self.name
def get_score(self, is_fast=True, ignore=False):
if ignore:
return 0
if self.is_first:
self.get_reference()
self.is_first = False
if is_fast:
return self.get_bleu_fast()
return self.get_bleu_parallel()
# fetch REAL DATA
def get_reference(self):
if self.reference is None:
reference = list()
with open(self.real_data) as real_data:
for text in real_data:
text = nltk.word_tokenize(text)
reference.append(text)
self.reference = reference
return reference
else:
return self.reference
def get_bleu(self):
raise Exception('make sure you call BLEU paralell')
ngram = self.gram
bleu = list()
reference = self.get_reference()
weight = tuple((1. / ngram for _ in range(ngram)))
with open(self.test_data) as test_data:
for hypothesis in test_data:
hypothesis = nltk.word_tokenize(hypothesis)
bleu.append(nltk.translate.bleu_score.sentence_bleu(reference, hypothesis, weight,
smoothing_function=SmoothingFunction().method1))
return sum(bleu) / len(bleu)
def calc_bleu(self, reference, hypothesis, weight):
return nltk.translate.bleu_score.sentence_bleu(reference, hypothesis, weight,
smoothing_function=SmoothingFunction().method1)
def get_bleu_fast(self):
reference = self.get_reference()
reference = reference[0:self.sample_size]
return self.get_bleu_parallel(reference=reference)
def get_bleu_parallel(self, reference=None):
ngram = self.gram
if reference is None:
reference = self.get_reference()
weight = tuple((1. / ngram for _ in range(ngram)))
pool = Pool(cpu_count())
result = list()
maxx = self.num_sentences
with open(self.test_data) as test_data:
for i, hypothesis in enumerate(test_data):
#print('i : {}'.format(i))
hypothesis = nltk.word_tokenize(hypothesis)
result.append(pool.apply_async(self.calc_bleu, args=(reference, hypothesis, weight)))
if i > maxx : break
score = 0.0
cnt = 0
for it, i in enumerate(result):
#print('i : {}'.format(it))
score += i.get()
cnt += 1
pool.close()
pool.join()
return score / cnt
class SelfBleu(Metrics):
def __init__(self, test_text='', gram=3, model_path='', num_sentences=500):
super(SelfBleu, self).__init__()
self.name = 'Self-Bleu'
self.test_data = test_text
self.gram = gram
self.sample_size = num_sentences
self.reference = None
self.is_first = True
def get_name(self):
return self.name
def get_score(self, is_fast=True, ignore=False):
if ignore:
return 0
if self.is_first:
self.get_reference()
self.is_first = False
if is_fast:
return self.get_bleu_fast()
return self.get_bleu_parallel()
def get_reference(self):
if self.reference is None:
reference = list()
with open(self.test_data) as real_data:
for text in real_data:
text = nltk.word_tokenize(text)
reference.append(text)
self.reference = reference
return reference
else:
return self.reference
def get_bleu(self):
ngram = self.gram
bleu = list()
reference = self.get_reference()
weight = tuple((1. / ngram for _ in range(ngram)))
with open(self.test_data) as test_data:
for hypothesis in test_data:
hypothesis = nltk.word_tokenize(hypothesis)
bleu.append(nltk.translate.bleu_score.sentence_bleu(reference, hypothesis, weight,
smoothing_function=SmoothingFunction().method1))
return sum(bleu) / len(bleu)
def calc_bleu(self, reference, hypothesis, weight):
return nltk.translate.bleu_score.sentence_bleu(reference, hypothesis, weight,
smoothing_function=SmoothingFunction().method1)
def get_bleu_fast(self):
reference = self.get_reference()
# random.shuffle(reference)
reference = reference[0:self.sample_size]
return self.get_bleu_parallel(reference=reference)
def get_bleu_parallel(self, reference=None):
ngram = self.gram
if reference is None:
reference = self.get_reference()
weight = tuple((1. / ngram for _ in range(ngram)))
pool = Pool(cpu_count())
result = list()
sentence_num = len(reference)
for index in range(sentence_num):
#genious:
hypothesis = reference[index]
other = reference[:index] + reference[index+1:]
result.append(pool.apply_async(self.calc_bleu, args=(other, hypothesis, weight)))
score = 0.0
cnt = 0
for i in result:
score += i.get()
cnt += 1
pool.close()
pool.join()
return score / cnt
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