在Python中使用LDA处理文本
[翻译] 在Python中使用LDA处理文本
发表于2个月前(2016-02-17 16:10) 阅读（
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安装 示例
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说明：
原文：http://chrisstrelioff.ws/sandbox/2014/11/13/getting_started_with_latent_dirichlet_allocation_in_python.html
本文包含了上文的主要内容。
关于LDA：LDA漫游指南
使用的python库lda来自：https://github.com/ariddell/lda 。
gensim库也含有lda相关函数。
$ pip install lda --user
from __future__ import division, print_function
import numpy as np
import lda
import lda.datasets
# document-term matrix
X = lda.datasets.load_reuters()
print("type(X): {}".format(type(X)))
print("shape: {}\n".format(X.shape))
print(X[:5, :5])
'''输出：
type(X):
shape: (395L, 4258L)
[[ 1 0 1 0 0]
[ 7 0 2 0 0]
[ 0 0 0 1 10]
[ 6 0 1 0 0]
[ 0 0 0 2 14]]
'''
X为395*4298的矩阵，意味着395个文本，共4258个单词。值代表出现次数。
看一下是哪些单词：
# the vocab
vocab = lda.datasets.load_reuters_vocab()
print("type(vocab): {}".format(type(vocab)))
print("len(vocab): {}\n".format(len(vocab)))
print(vocab[:6])
'''输出
type(vocab):
len(vocab): 4258
('church', 'pope', 'years', 'people', 'mother', 'last')
'''
X中第0列对应的单词是church，第1列对应的单词是pope
下面看一下文章标题：
# titles for each story
titles = lda.datasets.load_reuters_titles()
print("type(titles): {}".format(type(titles)))
print("len(titles): {}\n".format(len(titles)))
print(titles[:2]) # 前两篇文章的标题
'''输出
type(titles):
len(titles): 395
('0 UK: Prince Charles spearheads British royal revolution. LONDON 1996-08-20', '1 GERMANY: Historic Dresden church rising from WW2 ashes. DRESDEN, Germany 1996-08-21')
'''
训练数据，指定20个主题，500次迭代：
model = lda.LDA(n_topics=20, n_iter=500, random_state=1)
model.fit(X)
主题-单词（topic-word）分布：
topic_word = model.topic_word_
print("type(topic_word): {}".format(type(topic_word)))
print("shape: {}".format(topic_word.shape))
'''输出:
type(topic_word):
shape: (20L, 4258L)
'''
topic_word中一行对应一个topic，一行之和为1。 看一看'church', 'pope', 'years'这三个单词在各个主题中的比重：
print(topic_word[:, :3])
'''输出
[[ 2.72436509e-06 2.72436509e-06 2.72708945e-03]
[ 2.29518860e-02 1.08771556e-06 7.83263973e-03]
[ 3.97404221e-03 4.96135108e-06 2.98177200e-03]
[ 3.27374625e-03 2.72585033e-06 2.72585033e-06]
[ 8.26262882e-03 8.56893407e-02 1.61980569e-06]
[ 1.30107788e-02 2.95632328e-06 2.95632328e-06]
[ 2.80145003e-06 2.80145003e-06 2.80145003e-06]
[ 2.42858077e-02 4.66944966e-06 4.66944966e-06]
[ 6.84655429e-03 1.90129250e-06 6.84655429e-03]
[ 3.48361655e-06 3.48361655e-06 3.48361655e-06]
[ 2.98781661e-03 3.31611166e-06 3.31611166e-06]
[ 4.27062069e-06 4.27062069e-06 4.27062069e-06]
[ 1.50994982e-02 1.64107142e-06 1.64107142e-06]
[ 7.73480150e-07 7.73480150e-07 1.70946848e-02]
[ 2.82280146e-06 2.82280146e-06 2.82280146e-06]
[ 5.15309856e-06 5.15309856e-06 4.64294180e-03]
[ 3.41695768e-06 3.41695768e-06 3.41695768e-06]
[ 3.90980357e-02 1.70316633e-03 4.42279319e-03]
[ 2.39373034e-06 2.39373034e-06 2.39373034e-06]
[ 3.32493234e-06 3.32493234e-06 3.32493234e-06]]
'''
获取每个topic下权重最高的5个单词：
n = 5
for i, topic_dist in enumerate(topic_word):
topic_words = np.array(vocab)[np.argsort(topic_dist)][:-(n+1):-1]
print('*Topic {}\n- {}'.format(i, ' '.join(topic_words)))
'''输出：
*Topic 0
- government british minister west group
*Topic 1
- church first during people political
*Topic 2
- elvis king wright fans presley
*Topic 3
- yeltsin russian russia president kremlin
*Topic 4
- pope vatican paul surgery pontiff
*Topic 5
- family police miami versace cunanan
*Topic 6
- south simpson born york white
*Topic 7
- order church mother successor since
*Topic 8
- charles prince diana royal queen
*Topic 9
- film france french against actor
*Topic 10
- germany german war nazi christian
*Topic 11
- east prize peace timor quebec
*Topic 12
- n't told life people church
*Topic 13
- years world time year last
*Topic 14
- mother teresa heart charity calcutta
*Topic 15
- city salonika exhibition buddhist byzantine
*Topic 16
- music first people tour including
*Topic 17
- church catholic bernardin cardinal bishop
*Topic 18
- harriman clinton u.s churchill paris
*Topic 19
- century art million museum city
'''
文档-主题（Document-Topic）分布：
doc_topic = model.doc_topic_
print("type(doc_topic): {}".format(type(doc_topic)))
print("shape: {}".format(doc_topic.shape))
'''输出：
type(doc_topic):
shape: (395, 20)
'''
一篇文章对应一行，每行的和为1。
输入前10篇文章最可能的Topic：
for n in range(10):
topic_most_pr = doc_topic[n].argmax()
print("doc: {} topic: {}".format(n, topic_most_pr))
'''输出：
doc: 0 topic: 8
doc: 1 topic: 1
doc: 2 topic: 14
doc: 3 topic: 8
doc: 4 topic: 14
doc: 5 topic: 14
doc: 6 topic: 14
doc: 7 topic: 14
doc: 8 topic: 14
doc: 9 topic: 8
'''