SK-Learn使用NMF（非负矩阵分解）和LDA（隐含狄利克雷分布）进行话题抽取

 1 # Author: Olivier Grisel <olivier.grisel@ensta.org>
 2 #         Lars Buitinck <L.J.Buitinck@uva.nl>
 3 #         Chyi-Kwei Yau <chyikwei.yau@gmail.com>
 4 # License: BSD 3 clause
 5 
 6 from __future__ import print_function
 7 from time import time
 8 
 9 from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
10 from sklearn.decomposition import NMF, LatentDirichletAllocation
11 from sklearn.datasets import fetch_20newsgroups
12 
13 n_samples = 2000
14 n_features = 1000
15 n_topics = 10
16 n_top_words = 20
17 
18 
19 def print_top_words(model, feature_names, n_top_words):
20     for topic_idx, topic in enumerate(model.components_):
21         print("Topic #%d:" % topic_idx)
22         print(" ".join([feature_names[i]
23                         for i in topic.argsort()[:-n_top_words - 1:-1]]))
24     print()
25 
26 
27 # Load the 20 newsgroups dataset and vectorize it. We use a few heuristics
28 # to filter out useless terms early on: the posts are stripped of headers,
29 # footers and quoted replies, and common English words, words occurring in
30 # only one document or in at least 95% of the documents are removed.
31 
32 print("Loading dataset...")
33 t0 = time()
34 dataset = fetch_20newsgroups(shuffle=True, random_state=1,
35                              remove=('headers', 'footers', 'quotes'))
36 data_samples = dataset.data
37 print("done in %0.3fs." % (time() - t0))
38 
39 # Use tf-idf features for NMF.
40 print("Extracting tf-idf features for NMF...")
41 tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=2, #max_features=n_features,
42                                    stop_words='english')
43 t0 = time()
44 tfidf = tfidf_vectorizer.fit_transform(data_samples)
45 print("done in %0.3fs." % (time() - t0))
46 
47 # Use tf (raw term count) features for LDA.
48 print("Extracting tf features for LDA...")
49 tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2, max_features=n_features,
50                                 stop_words='english')
51 t0 = time()
52 tf = tf_vectorizer.fit_transform(data_samples)
53 print("done in %0.3fs." % (time() - t0))
54 
55 # Fit the NMF model
56 print("Fitting the NMF model with tf-idf features,"
57       "n_samples=%d and n_features=%d..."
58       % (n_samples, n_features))
59 t0 = time()
60 nmf = NMF(n_components=n_topics, random_state=1, alpha=.1, l1_ratio=.5).fit(tfidf)
61 exit()
62 print("done in %0.3fs." % (time() - t0))
63 
64 print("\nTopics in NMF model:")
65 tfidf_feature_names = tfidf_vectorizer.get_feature_names()
66 print_top_words(nmf, tfidf_feature_names, n_top_words)
67 
68 print("Fitting LDA models with tf features, n_samples=%d and n_features=%d..."
69       % (n_samples, n_features))
70 lda = LatentDirichletAllocation(n_topics=n_topics, max_iter=5,
71                                 learning_method='online', learning_offset=50.,
72                                 random_state=0)
73 t0 = time()
74 lda.fit(tf)
75 print("done in %0.3fs." % (time() - t0))
76 
77 print("\nTopics in LDA model:")
78 tf_feature_names = tf_vectorizer.get_feature_names()
79 print_top_words(lda, tf_feature_names, n_top_words)

Loading dataset...
done in 2.222s.
Extracting tf-idf features for NMF...
done in 2.730s.
Extracting tf features for LDA...
done in 2.702s.
Fitting the NMF model with tf-idf features,n_samples=2000 and n_features=1000...
done in 1.904s.

Topics in NMF model:
Topic #0:
don just people think like know good time right ve say did make really way want going new year ll
Topic #1:
windows thanks file card does dos mail files know program use advance hi window help software looking ftp video pc
Topic #2:
drive scsi ide drives disk controller hard floppy bus hd cd boot mac cable card isa rom motherboard mb internal
Topic #3:
key chip encryption clipper keys escrow government algorithm security secure encrypted public nsa des enforcement law privacy bit use secret
Topic #4:
00 sale 50 shipping 20 10 price 15 new 25 30 dos offer condition 40 cover asking 75 01 interested
Topic #5:
armenian armenians turkish genocide armenia turks turkey soviet people muslim azerbaijan russian greek argic government serdar kurds population ottoman million
Topic #6:
god jesus bible christ faith believe christians christian heaven sin life hell church truth lord does say belief people existence
Topic #7:
mouse driver keyboard serial com1 port bus com3 irq button com sys microsoft ball problem modem adb drivers card com2
Topic #8:
space nasa shuttle launch station sci gov orbit moon earth lunar satellite program mission center cost research data solar mars
Topic #9:
msg food chinese flavor eat glutamate restaurant foods reaction taste restaurants salt effects carl brain people ingredients natural causes olney

Fitting LDA models with tf features, n_samples=2000 and n_features=1000...
done in 22.548s.

Topics in LDA model:
Topic #0:
government people mr law gun state president states public use right rights national new control american security encryption health united
Topic #1:
drive card disk bit scsi use mac memory thanks pc does video hard speed apple problem used data monitor software
Topic #2:
said people armenian armenians turkish did saw went came women killed children turkey told dead didn left started greek war
Topic #3:
year good just time game car team years like think don got new play games ago did season better ll
Topic #4:
10 00 15 25 12 11 20 14 17 16 db 13 18 24 30 19 27 50 21 40
Topic #5:
windows window program version file dos use files available display server using application set edu motif package code ms software
Topic #6:
edu file space com information mail data send available program ftp email entry info list output nasa address anonymous internet
Topic #7:
ax max b8f g9v a86 pl 145 1d9 0t 34u 1t 3t giz bhj wm 2di 75u 2tm bxn 7ey
Topic #8:
god people jesus believe does say think israel christian true life jews did bible don just know world way church
Topic #9:
don know like just think ve want does use good people key time way make problem really work say need