Python:Gensim内存错误

问题描述

import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
from gensim import corpora, models, similarities
from nltk.corpus import stopwords
import codecs

documents = []
with codecs.open("Master_File_for_Docs.txt", encoding = 'utf-8', mode= "r") as fid:
   for line in fid:
       documents.append(line)
stoplist = []
x = stopwords.words('english')
for word in x:
    stoplist.append(word)

#Removes Stopwords
texts = [[word for word in document.lower().split() if word not in stoplist]
for document in documents]


dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]

lda = models.LdaModel(corpus, id2word=dictionary, num_topics=100)
lda.print_topics(20)
#corpus_lda = lda[corpus]
#for doc in corpus_lda:
 #   print(doc)

我正在运行Gensim进行主题建模，并尝试使上述代码正常工作.我知道这段代码行得通，因为我的朋友从Mac计算机上运行了该代码，并且运行成功，但是当我从Windows计算机上运行该代码时，我得到了

I am running Gensim for topic modeling and trying to get the above code working. I know that this code works because my friend ran it from a mac computer and it worked successfully but when I run it from a windows computer the code gives me a

MemoryError

我在第二行中设置的日志记录也没有出现在Windows计算机上.为了让gensim工作，Windows中是否需要修复某些东西?

Also the logging that I set on the second line also doesn't appear on my windows computer. Is there something in Windows that I need to fix in order for gensim to work?

推荐答案

出现 MemoryError 是因为Gensim在分析数据时会尝试将所需的所有数据保留在内存中.解决方案很简单:

The MemoryError appears because Gensim is trying to keep all of the data you need in memory while analyzing it.The solutions are scarse:

• 使用具有更多内存的服务器(AWS计算机，比您的PC更强大的功能)
• 尝试使用64位python解释器
• 尝试减小 model.save()中的 size 参数.这样会减少代表您的单词的功能
• 尝试增加 model.save()中的 min_count 参数.这将使模型只考虑出现至少 min_count 次
的单词

• Use a server with more memory (AWS machine, anything more powerful than your PC)
• Try a python interpreter in 64 bit
• Try reducing the size parameter in model.save(). This will lead to have less features representing your words
• Try increasing the min_count parameter in model.save(). This will make the model consider only words that appear at least min_count times

请注意，这最后两个建议会修改模型的特征

Be careful though, these last 2 advices will modify the characteristics of your model

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