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Python3.5 数据处理 --jieba + sklearn库安装及第一个示例

一,安装pip3:

#sudo apt-get install pip3

二,安装jieba:

#sudo pip3 install jieba

三,安装sklearn:

#sudo pip3 install scikit-learn

四,安装sklearn依赖(numpy,scipy):

#sudo pip3 install numpy
#sudo pip3 install scipy

eg:国内安装时可能出现time-out错误---解决办法如下:

#sudo pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple numpy
#sudo pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple scipy

五,简单实现分词并计算TF-IDF值:

技术分享

#!/usr/bin python3.5
# coding=utf-8
#import os
import jieba
import re
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import CountVectorizer
#import collections


class Tfi(object):
    def __init__(self):
        self.stop_list = []
        pass

    def fenci(self, file):
        #list = []
        fin = open(file, r)
        read_b = fin.read()
        fin.close()
        read_res = ‘‘.join(re.findall(u[a-zA-Z0-9\u4e00-\u9fa5]+, read_b))
        cut_res = jieba.cut(read_res, cut_all=True)
        line_res = ‘‘
        for i in cut_res:
            if i not in self.stop_list:
                line_res = line_res + i +  
        fout = open(res/ + file, w)
        fout.write(line_res)
        fout.close()

    def cipin(self, fil_list):
        corpus = []
        for fil in fil_list:
            ffout = open(res/+fil, r)
            read_r = ffout.read()
            ffout.close()
            corpus.append(read_r)
        vectorizer = CountVectorizer()
        transformer = TfidfTransformer()
        tfidf = transformer.fit_transform(vectorizer.fit_transform(corpus))
        word = vectorizer.get_feature_names()  # 所有文本的关键字
        weight = tfidf.toarray()
        for j in range(len(weight)):
            f = open(fes/+fil_list[j], w)
            for i in range(len(word)):
                f.write(word[i]+  +str(weight[j][i]) + \n)
            f.close()


if __name__ == __main__:
    first = Tfi()
    fil_list = [inputtext]
    first.fenci(inputtext)
    first.cipin(fil_list)

 

Python3.5 数据处理 --jieba + sklearn库安装及第一个示例