nltk-构建和使用语料库-可用于小说的推荐

结合先前的python文章现在我们可以进一步的通过自然语言处理来实操演练，应用到网站中，更好的辅助seo
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 https://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

原文：深蓝苹果 https://my.oschina.net/kakablue/home