到2018年3月7日為止，本系列三篇文章已寫完，可能後續有新的內容的話會繼續更新。

本篇是我的LDA主題挖掘系列的第二篇，介紹如何利用gensim包提供的方法來訓練自己處理好的語料。
gensim提供了多種方法：

速度較慢的：

from gensim.models.ldamodel import LdaModel
# 利用處理好的語料訓練模型
lda = LdaModel(corpus, num_topics=10)
# 推斷新文字的主題分佈
doc_lda = lda[doc_bow]
# 用新語料更新模型
lda.update(other_corpus)
 

速度較快，使用多核心的：

>>> from gensim import corpora, models 
>>> lda = LdaMulticore(corpus, id2word=id2word, num_topics=100)  # train model
>>> print(lda[doc_bow]) # get topic probability distribution for a document
>>> lda.update(corpus2) # update the LDA model with additional documents
 

>>> print(lda[doc_bow])

使用多程序對效能的提升：

Wall-clock performance on the English Wikipedia (2G corpus positions, 3.5M documents, 100K features, 0.54G non-zero entries in the final bag-of-words matrix), requesting 100 topics:
(Measured on this i7 server with 4 physical cores, so that optimal workers=3, one less than the number of cores.)

workers的值需要比電腦的核心數小1
本文程式碼使用多核心的方法。
有問題歡迎留言交流。
本文在將語料轉化為corpus後，進行了如下操作：

tfidf = models.TfidfModel(corpus)
corpusTfidf = tfidf[corpus]

這一步是用來調整語料中不同詞的詞頻，將那些在所有文件中都出現的高頻詞的詞頻降低，具體原理可參見阮一峰老師的系列部落格：TF-IDF與餘弦相似性的應用（一）：自動提取關鍵詞，我經過這一步處理後，貌似效果提升不明顯，而且這一步時間消耗較大，不建議採用。可直接將corpus作為訓練資料傳入lda模型中。

#-*-coding:utf-8-*-
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
import os
import codecs
from gensim.corpora import Dictionary
from gensim import corpora, models
from datetime import datetime
import platform
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s : ', level=logging.INFO)

platform_info = platform.platform().lower()
if 'windows' in platform_info:
    code = 'gbk'
elif 'linux' in platform_info:
    code = 'utf-8'
path = sys.path[0]

class GLDA(object):
    """docstring for GdeltGLDA"""

    def __init__(self, stopfile=None):
        super(GLDA, self).__init__()
        if stopfile:
            with codecs.open(stopfile, 'r', code) as f:
                self.stopword_list = f.read().split(' ')
            print ('the num of stopwords is : %s'%len(self.stopword_list))
        else:
            self.stopword_list = None

    def lda_train(self, num_topics, datafolder, middatafolder, dictionary_path=None, corpus_path=None, iterations=5000, passes=1, workers=3):       
        time1 = datetime.now()
        num_docs = 0
        doclist = []
        if not corpus_path or not dictionary_path: # 若無字典或無corpus，則讀取預處理後的docword。一般第一次執行都需要讀取，在後期調參時，可直接傳入字典與corpus路徑
            for filename in os.listdir(datafolder): # 讀取datafolder下的語料
                with codecs.open(datafolder+filename, 'r', code) as source_file:
                    for line in source_file:
                        num_docs += 1
                        if num_docs%100000==0:
                            print ('%s, %s'%(filename, num_docs))
                        #doc = [word for word in doc if word not in self.stopword_list]
                        doclist.append(line.split(' '))
                print ('%s, %s'%(filename, num_docs))
        if dictionary_path:
            dictionary = corpora.Dictionary.load(dictionary_path) # 載入字典
        else:            
            #構建詞彙統計向量並儲存
            dictionary = corpora.Dictionary(doclist)
            dictionary.save(middatafolder + 'dictionary.dictionary')
        if corpus_path:
            corpus = corpora.MmCorpus(corpus_path) # 載入corpus
        else:
            corpus = [dictionary.doc2bow(doc) for doc in doclist]
            corpora.MmCorpus.serialize(middatafolder + 'corpus.mm', corpus) # 儲存corpus
        tfidf = models.TfidfModel(corpus)
        corpusTfidf = tfidf[corpus]
        time2 = datetime.now()
        lda_multi = models.ldamulticore.LdaMulticore(corpus=corpusTfidf, id2word=dictionary, num_topics=num_topics, \
            iterations=iterations, workers=workers, batch=True, passes=passes) # 開始訓練
        lda_multi.print_topics(num_topics, 30) # 輸出主題詞矩陣
        print ('lda training time cost is : %s, all time cost is : %s '%(datetime.now()-time2, datetime.now()-time1))
        #模型的儲存/ 載入
        lda_multi.save(middatafolder + 'lda_tfidf_%s_%s.model'%(2014, num_topics, iterations)) # 儲存模型
        # lda = models.ldamodel.LdaModel.load('zhwiki_lda.model') # 載入模型
        # save the doc-topic-id
        topic_id_file = codecs.open(middatafolder + 'topic.json', 'w', 'utf-8')
        for i in range(num_docs):
            topic_id = lda_multi[corpusTfidf[i]][0][0] # 取概率最大的主題作為文字所屬主題
            topic_id_file.write(str(topic_id)+ ' ')

if __name__ == '__main__':
    datafolder = path + os.sep + 'docword' + os.sep # 預處理後的語料所在資料夾，函式會讀取此資料夾下的所有語料檔案
    middatafolder = path + os.sep + 'middata' + os.sep
    dictionary_path = middatafolder + 'dictionary.dictionary' # 已處理好的字典，若無，則設定為False
    corpus_path = middatafolder + 'corpus.mm' # 對語料處理過後的corpus，若無，則設定為False
    # stopfile = path + os.sep + 'rest_stopwords.txt' # 新新增的停用詞檔案
    num_topics = 50
    passes = 2 # 這個引數大概是將全部語料進行訓練的次數，數值越大，引數更新越多，耗時更長
    iterations = 6000
    workers = 3 # 相當於程序數
    lda = GLDA()
    lda.lda_train(num_topics, datafolder, middatafolder, dictionary_path=dictionary_path, corpus_path=corpus_path, iterations=iterations, passes=passes, workers=workers)

以上，歡迎交流與指正。