import os
from sklearn.datasets.base import Bunch
import pickle#持久化类
from sklearn import feature_extraction
from sklearn.feature_extraction.text import TfidfTransformer#TF-IDF向量转换类
from sklearn.feature_extraction.text import TfidfVectorizer#TF-IDF向量生成类

def readbunchobj(path):
    file_obj=open(path,"rb")
    bunch=pickle.load(file_obj)
    file_obj.close()
    return bunch
def writebunchobj(path,bunchobj):
    file_obj=open(path,"wb")
    pickle.dump(bunchobj,file_obj)
    file_obj.close()

def readfile(path):
    fp = open(path, "r", encoding='gb2312', errors='ignore')
    content = fp.read()
    fp.close()
    return content


path="train_word_bag/train_set.dat"
bunch=readbunchobj(path)

#停用词
stopword_path="stop_words.txt"
stpwrdlst=readfile(stopword_path).splitlines()
#构建TF-IDF词向量空间对象
tfidfspace=Bunch(target_name=bunch.target_name,label=bunch.label,filenames=bunch.filenames,tdm=[],vocabulary={})
#使用TfidVectorizer初始化向量空间模型
vectorizer=TfidfVectorizer(stop_words=stpwrdlst,sublinear_tf=True,max_df=0.5)
transfoemer=TfidfTransformer()#该类会统计每个词语的TF-IDF权值

#文本转为词频矩阵，单独保存字典文件
tfidfspace.tdm=vectorizer.fit_transform(bunch.contents)
tfidfspace.vocabulary=vectorizer.vocabulary_

#创建词袋的持久化
space_path="train_word_bag/tfidfspace.dat"
writebunchobj(space_path,tfidfspace)
9cc2c264b45cdb6da999d514e754b03bd18b2d8a
<mask token>


def writebunchobj(path, bunchobj):
    file_obj = open(path, 'wb')
    pickle.dump(bunchobj, file_obj)
    file_obj.close()


def readfile(path):
    fp = open(path, 'r', encoding='gb2312', errors='ignore')
    content = fp.read()
    fp.close()
    return content


<mask token>

<mask token>


def readbunchobj(path):
    file_obj = open(path, 'rb')
    bunch = pickle.load(file_obj)
    file_obj.close()
    return bunch


def writebunchobj(path, bunchobj):
    file_obj = open(path, 'wb')
    pickle.dump(bunchobj, file_obj)
    file_obj.close()


def readfile(path):
    fp = open(path, 'r', encoding='gb2312', errors='ignore')
    content = fp.read()
    fp.close()
    return content


<mask token>

<mask token>


def readbunchobj(path):
    file_obj = open(path, 'rb')
    bunch = pickle.load(file_obj)
    file_obj.close()
    return bunch


def writebunchobj(path, bunchobj):
    file_obj = open(path, 'wb')
    pickle.dump(bunchobj, file_obj)
    file_obj.close()


def readfile(path):
    fp = open(path, 'r', encoding='gb2312', errors='ignore')
    content = fp.read()
    fp.close()
    return content


path = 'train_word_bag/train_set.dat'
bunch = readbunchobj(path)
stopword_path = 'stop_words.txt'
stpwrdlst = readfile(stopword_path).splitlines()
tfidfspace = Bunch(target_name=bunch.target_name, label=bunch.label,
    filenames=bunch.filenames, tdm=[], vocabulary={})
vectorizer = TfidfVectorizer(stop_words=stpwrdlst, sublinear_tf=True,
    max_df=0.5)
transfoemer = TfidfTransformer()
tfidfspace.tdm = vectorizer.fit_transform(bunch.contents)
tfidfspace.vocabulary = vectorizer.vocabulary_
space_path = 'train_word_bag/tfidfspace.dat'
writebunchobj(space_path, tfidfspace)

import os
from sklearn.datasets.base import Bunch
import pickle
from sklearn import feature_extraction
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer


def readbunchobj(path):
    file_obj = open(path, 'rb')
    bunch = pickle.load(file_obj)
    file_obj.close()
    return bunch


def writebunchobj(path, bunchobj):
    file_obj = open(path, 'wb')
    pickle.dump(bunchobj, file_obj)
    file_obj.close()


def readfile(path):
    fp = open(path, 'r', encoding='gb2312', errors='ignore')
    content = fp.read()
    fp.close()
    return content


path = 'train_word_bag/train_set.dat'
bunch = readbunchobj(path)
stopword_path = 'stop_words.txt'
stpwrdlst = readfile(stopword_path).splitlines()
tfidfspace = Bunch(target_name=bunch.target_name, label=bunch.label,
    filenames=bunch.filenames, tdm=[], vocabulary={})
vectorizer = TfidfVectorizer(stop_words=stpwrdlst, sublinear_tf=True,
    max_df=0.5)
transfoemer = TfidfTransformer()
tfidfspace.tdm = vectorizer.fit_transform(bunch.contents)
tfidfspace.vocabulary = vectorizer.vocabulary_
space_path = 'train_word_bag/tfidfspace.dat'
writebunchobj(space_path, tfidfspace)

import os
from sklearn.datasets.base import Bunch
import pickle#持久化类
from sklearn import feature_extraction
from sklearn.feature_extraction.text import TfidfTransformer#TF-IDF向量转换类
from sklearn.feature_extraction.text import TfidfVectorizer#TF-IDF向量生成类

def readbunchobj(path):
    file_obj=open(path,"rb")
    bunch=pickle.load(file_obj)
    file_obj.close()
    return bunch
def writebunchobj(path,bunchobj):
    file_obj=open(path,"wb")
    pickle.dump(bunchobj,file_obj)
    file_obj.close()

def readfile(path):
    fp = open(path, "r", encoding='gb2312', errors='ignore')
    content = fp.read()
    fp.close()
    return content


path="train_word_bag/train_set.dat"
bunch=readbunchobj(path)

#停用词
stopword_path="stop_words.txt"
stpwrdlst=readfile(stopword_path).splitlines()
#构建TF-IDF词向量空间对象
tfidfspace=Bunch(target_name=bunch.target_name,label=bunch.label,filenames=bunch.filenames,tdm=[],vocabulary={})
#使用TfidVectorizer初始化向量空间模型
vectorizer=TfidfVectorizer(stop_words=stpwrdlst,sublinear_tf=True,max_df=0.5)
transfoemer=TfidfTransformer()#该类会统计每个词语的TF-IDF权值

#文本转为词频矩阵，单独保存字典文件
tfidfspace.tdm=vectorizer.fit_transform(bunch.contents)
tfidfspace.vocabulary=vectorizer.vocabulary_

#创建词袋的持久化
space_path="train_word_bag/tfidfspace.dat"
writebunchobj(space_path,tfidfspace)