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
|
#! /usr/bin/env python
#-*- coding: utf-8 -*-
import itertools
import nltk, string
from sklearn.feature_extraction.text import TfidfVectorizer
import utils
# tokenizers/punkt/english.pickle
stemmer = nltk.stem.porter.PorterStemmer()
remove_punctuation_map = dict((ord(char), None) for char in string.punctuation)
def stem_tokens(tokens):
return [stemmer.stem(item) for item in tokens]
def normalize(text):
"""
Remove punctuation, lowercase, stem
"""
return stem_tokens(nltk.word_tokenize(text.lower().translate(remove_punctuation_map)))
vectorizer = TfidfVectorizer(tokenizer=normalize, stop_words='english')
def cosine_sim(article1, article2):
try:
tfidf = vectorizer.fit_transform([utils.clear_string(article1.content),
utils.clear_string(article2.content)])
except ValueError as e:
raise e
return ((tfidf * tfidf.T).A)[0,1]
def compare_documents(feed):
"""
Compare a list of documents by pair.
"""
nltk.download("punkt")
duplicates = []
for pair in [(elem[0], elem[1]) for elem in itertools.product(feed.articles, repeat=2)
if elem[0].id != elem[1].id]:
try:
result = cosine_sim(*pair)
if abs(result.item() - 1.0) < 1e-10:
duplicates.append(pair)
#print pair[0].id, pair[0].title, pair[0].link
#print pair[1].id, pair[1].title, pair[1].link
#print
except ValueError:
continue
return duplicates
|
