NLTK的高效术语文档矩阵 [英] efficient Term Document Matrix with NLTK

问题描述

我正在尝试使用NLTK和熊猫创建术语文档矩阵. 我写了以下函数:

I am trying to create a term document matrix with NLTK and pandas. I wrote the following function:

def fnDTM_Corpus(xCorpus):
    import pandas as pd
    '''to create a Term Document Matrix from a NLTK Corpus'''
    fd_list = []
    for x in range(0, len(xCorpus.fileids())):
        fd_list.append(nltk.FreqDist(xCorpus.words(xCorpus.fileids()[x])))
    DTM = pd.DataFrame(fd_list, index = xCorpus.fileids())
    DTM.fillna(0,inplace = True)
    return DTM.T

运行

import nltk
from nltk.corpus import PlaintextCorpusReader
corpus_root = 'C:/Data/'

newcorpus = PlaintextCorpusReader(corpus_root, '.*')

x = fnDTM_Corpus(newcorpus)

它对于语料库中的一些小文件效果很好，但是当我尝试使用4,000个文件(每个约2 kb)的语料库运行它时，出现了 MemoryError .

It works well for few small files in the corpus but gives me a MemoryError when I try to run it with a corpus of 4,000 files (of about 2 kb each).

我想念什么吗?

我正在使用32位python. (在Windows 7、64位OS，Core Quad CPU，8 GB RAM上).我真的需要对这种大小的语料库使用64位吗?

I am using a 32 bit python. (am on windows 7, 64-bit OS, Core Quad CPU, 8 GB RAM). Do I really need to use 64 bit for corpus of this size ?

推荐答案

感谢Radim和Larsmans. 我的目标是要拥有一个与您在R tm中获得的DTM类似的DTM. 我决定使用scikit-learn，部分受

Thanks to Radim and Larsmans. My objective was to have a DTM like the one you get in R tm. I decided to use scikit-learn and partly inspired by this blog entry. This the code I came up with.

我将其发布在这里，希望其他人会发现它有用.

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer 

def fn_tdm_df(docs, xColNames = None, **kwargs):
    ''' create a term document matrix as pandas DataFrame
    with **kwargs you can pass arguments of CountVectorizer
    if xColNames is given the dataframe gets columns Names'''

    #initialize the  vectorizer
    vectorizer = CountVectorizer(**kwargs)
    x1 = vectorizer.fit_transform(docs)
    #create dataFrame
    df = pd.DataFrame(x1.toarray().transpose(), index = vectorizer.get_feature_names())
    if xColNames is not None:
        df.columns = xColNames

    return df

在目录中的文本列表上使用它

to use it on a list of text in a directory

DIR = 'C:/Data/'

def fn_CorpusFromDIR(xDIR):
    ''' functions to create corpus from a Directories
    Input: Directory
    Output: A dictionary with 
             Names of files ['ColNames']
             the text in corpus ['docs']'''
    import os
    Res = dict(docs = [open(os.path.join(xDIR,f)).read() for f in os.listdir(xDIR)],
               ColNames = map(lambda x: 'P_' + x[0:6], os.listdir(xDIR)))
    return Res

创建数据框

d1 = fn_tdm_df(docs = fn_CorpusFromDIR(DIR)['docs'],
          xColNames = fn_CorpusFromDIR(DIR)['ColNames'], 
          stop_words=None, charset_error = 'replace')  

这篇关于NLTK的高效术语文档矩阵的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！