nltk-构建和使用语料库-可用于小说的推荐-完整实例

步骤1：构建语料库：

#!/usr/bin/env python
#-*-coding=utf-8-*-

#数据源目录(二级目录)
sourceDataDir='data'

#数据源文件列表
fileLists = []

import os
from gensim import corpora, models, similarities

def getSourceFileLists(sourceDataDir):
fileLists = []
subDirList = os.listdir(sourceDataDir)
for subDir in subDirList:
subList = os.listdir(sourceDataDir + '/' + subDir)
fileList = [ sourceDataDir+'/'+subDir+'/'+ x for x in subList if os.path.isfile(sourceDataDir+'/'+subDir+'/'+x)]
fileLists += fileList

return fileLists

fileLists = getSourceFileLists(sourceDataDir)

if 0 < len(fileLists):
import codecs
import jieba
punctuations = ['','\n','\t',',', '.', ':', ';', '?', '(', ')', '[', ']', '&', '!', '*', '@', '#', '$', '%']

if not os.path.exists('dict'):
os.mkdir("dict")
if not os.path.exists('corpus'):
os.mkdir("corpus")

for fileName in fileLists:
print fileName

hFile = None
content = None
try:
hFile = codecs.open(fileName,'r','gb18030')
content = hFile.readlines()
except Exception,e:
print e
finally:
if hFile:
hFile.close()

if content:
fileFenci = [ x for x in jieba.cut(' '.join(content),cut_all=True)]
fileFenci2 = [word for word in fileFenci if not word in punctuations]

texts = [fileFenci2]

all_tokens = sum(texts, [])
tokens_once = set(word for word in set(all_tokens) if all_tokens.count(word) == 1)
texts = [[word for word in text if word not in tokens_once] for text in texts]

sFileDir, sFileName = os.path.split(fileName)
dictFileName = 'dict/'+sFileName+'.dict'
corpusFileName = 'corpus/'+sFileName+'.mm'

dictionary = corpora.Dictionary(texts)
dictionary.save_as_text(dictFileName)

corpus = ([dictionary.doc2bow(text) for text in texts])
corpora.MmCorpus.serialize(corpusFileName, corpus)

print 'Build corpus done'

数据源：

来自 http://d1.txthj.com/newrar/txthj_264.rar 的83篇小说，将其目录存放在目录 ./data/下。

加载时作为二层目录处理

输出：

./dict 和 ./corpus

在对应目录下生成 xxx.dict 和 xxx.mm，xxx为原文件的全称(不包括路径，包括后缀)

步骤2：加载语料库，相似性分析

#!/usr/bin/env python
#-*-coding=utf-8-*-

import os
from gensim import corpora, models, similarities

def getFileList(dir):
return [ dir + x for x in os.listdir(dir)]
dictLists =  getFileList('./dict/')

class LoadDictionary(object):
def __init__(self, dictionary):
self.dictionary = dictionary

def __iter__(self):
for dictFile in dictLists:
sFileRaw, sFilePostfix = os.path.splitext(dictFile)
sFileDir, sFileName = os.path.split(sFileRaw)
(dictFile, corpusFile) = ( './dict/' + sFileName + '.dict',  './corpus/'+sFileName + '.mm')
yield self.dictionary.load_from_text(dictFile)

class LoadCorpus(object):

def __iter__(self):
for dictFile in dictLists:
sFileRaw, sFilePostfix = os.path.splitext(dictFile)
sFileDir, sFileName = os.path.split(sFileRaw)
(dictFile, corpusFile) = ( './dict/' + sFileName + '.dict',  './corpus/'+sFileName + '.mm')
yield corpora.MmCorpus(corpusFile)

"""
预处理(easy_install nltk)
"""
#简化的 中文+英文 预处理
def pre_process_cn(inputs, low_freq_filter = True):
"""
1.去掉停用词
2.去掉标点符号
3.处理为词干
4.去掉低频词

"""
import nltk
import jieba.analyse
from nltk.tokenize import word_tokenize

texts_tokenized = []
for document in inputs:
texts_tokenized_tmp = []
for word in word_tokenize(document):
texts_tokenized_tmp += jieba.analyse.extract_tags(word,10)
texts_tokenized.append(texts_tokenized_tmp)

texts_filtered_stopwords = texts_tokenized

#去除标点符号
english_punctuations = [',', '.', ':', ';', '?', '(', ')', '[', ']', '&', '!', '*', '@', '#', '$', '%']
texts_filtered = [[word for word in document if not word in english_punctuations] for document in texts_filtered_stopwords]

#词干化
from nltk.stem.lancaster import LancasterStemmer
st = LancasterStemmer()
texts_stemmed = [[st.stem(word) for word in docment] for docment in texts_filtered]

#去除过低频词
if low_freq_filter:
all_stems = sum(texts_stemmed, [])
stems_once = set(stem for stem in set(all_stems) if all_stems.count(stem) == 1)
texts = [[stem for stem in text if stem not in stems_once] for text in texts_stemmed]
else:
texts = texts_stemmed
return texts

dictionary = corpora.dictionary.Dictionary()
dictionary_memory_friendly = LoadDictionary(dictionary)
for vector in dictionary_memory_friendly:
dictionary = vector

corpus = []
corpus_memory_friendly = LoadCorpus()
for vector in corpus_memory_friendly:
corpus.append(vector[0])

if 0 < len(corpus):
tfidf = models.TfidfModel(corpus)
corpus_tfidf = tfidf[corpus]

model = models.LsiModel(corpus_tfidf, id2word=None, num_topics=20,  chunksize=2000000) #不指定 id2word=dictionary 时，LsiModel内部会根据 corpus 重建 dictionary
index = similarities.Similarity('./novel_', model[corpus], num_features=len(corpus))

#要处理的对象登场，这里随便从小说中截取了一段话
target_courses = ['男人们的脸上沉重而冷凝，蒙着面纱的女人们则是发出断断续续的哭泣声，他们无比专注地看着前方，见证一场生与死的拉锯战。']
target_text = pre_process_cn(target_courses, low_freq_filter=False)

"""
对具体对象相似度匹配
"""
#选择一个基准数据
ml_course = target_text[0]
#词袋处理
ml_bow = dictionary.doc2bow(ml_course)

#在上面选择的模型数据 lsi model 中，计算其他数据与其的相似度
ml_lsi = model[ml_bow]     #ml_lsi 形式如 (topic_id, topic_value)
sims = index[ml_lsi]     #sims 是最终结果了， index[xxx] 调用内置方法 __getitem__() 来计算ml_lsi

#排序，为输出方便
sort_sims = sorted(enumerate(sims), key=lambda item: -item[1])

#查看结果
print sort_sims[0:10]
print len(dictLists)
print dictLists[sort_sims[1][0]]
print dictLists[sort_sims[2][0]]
print dictLists[sort_sims[3][0]]

说明：

yield的使用是为了更好的内存效率。

遗留问题：

    步骤2会有提示：

     /usr/lib/python2.7/dist-packages/scipy/sparse/compressed.py:122: UserWarning: indices array has non-integer dtype (float64)

    不影响处理过程