决策树（CART算法）针对中文文本分类

改编自博客：

http://blog.csdn.net/github_36326955/article/details/54891204

根据下面的参考了链接可知，sklearn中的决策树用的是CART算法

http://sofasofa.io/forum_main_post.php?postid=1000402&

做个笔记

代码按照1 2 3 4的顺序进行即可：

1.py(corpus_segment.py)

[python] view plain copy

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
@version: python2.7.8
@author: XiangguoSun
@contact: sunxiangguodut@qq.com
@file: corpus_segment.py
@time: 2017/2/5 15:28
@software: PyCharm
"""
import sys
import os
import jieba
# 配置utf-8输出环境
reload(sys)
sys.setdefaultencoding('utf-8')
# 保存至文件
def savefile(savepath, content):
with open(savepath, "wb") as fp:
fp.write(content)
'''''
上面两行是python2.6以上版本增加的语法，省略了繁琐的文件close和try操作
2.5版本需要from __future__ import with_statement
新手可以参考这个链接来学习http://zhoutall.com/archives/325
'''
# 读取文件
def readfile(path):
with open(path, "rb") as fp:
content = fp.read()
return content
def corpus_segment(corpus_path, seg_path):
'''''
corpus_path是未分词语料库路径
seg_path是分词后语料库存储路径
'''
catelist = os.listdir(corpus_path) # 获取corpus_path下的所有子目录
'''''
其中子目录的名字就是类别名，例如：
train_corpus/art/21.txt中，'train_corpus/'是corpus_path，'art'是catelist中的一个成员
'''
# 获取每个目录（类别）下所有的文件
for mydir in catelist:
'''''
这里mydir就是train_corpus/art/21.txt中的art（即catelist中的一个类别）
'''
class_path = corpus_path + mydir + "/" # 拼出分类子目录的路径如：train_corpus/art/
seg_dir = seg_path + mydir + "/" # 拼出分词后存贮的对应目录路径如：train_corpus_seg/art/
if not os.path.exists(seg_dir): # 是否存在分词目录，如果没有则创建该目录
os.makedirs(seg_dir)
file_list = os.listdir(class_path) # 获取未分词语料库中某一类别中的所有文本
'''''
train_corpus/art/中的
21.txt,
22.txt,
23.txt
...
file_list=['21.txt','22.txt',...]
'''
for file_path in file_list: # 遍历类别目录下的所有文件
fullname = class_path + file_path # 拼出文件名全路径如：train_corpus/art/21.txt
content = readfile(fullname) # 读取文件内容
'''''此时，content里面存贮的是原文本的所有字符，例如多余的空格、空行、回车等等，
接下来，我们需要把这些无关痛痒的字符统统去掉，变成只有标点符号做间隔的紧凑的文本内容
'''
content = content.replace("\r\n", "") # 删除换行
content = content.replace(" ", "")#删除空行、多余的空格
content_seg = jieba.cut(content) # 为文件内容分词
savefile(seg_dir + file_path, " ".join(content_seg)) # 将处理后的文件保存到分词后语料目录
print "中文语料分词结束！！！"
'''''
如果你对if __name__=="__main__":这句不懂，可以参考下面的文章
http://imoyao.lofter.com/post/3492bc_bd0c4ce
简单来说如果其他python文件调用这个文件的函数，或者把这个文件作为模块
导入到你的工程中时，那么下面的代码将不会被执行，而如果单独在命令行中
运行这个文件，或者在IDE（如pycharm）中运行这个文件时候，下面的代码才会运行。
即，这部分代码相当于一个功能测试。
如果你还没懂，建议你放弃IT这个行业。
'''
if __name__=="__main__":
#对训练集进行分词
corpus_path = "/home/appleyuchi/PycharmProjects/MultiNB/csdn_blog/54891204_tenwhy/chinese_text_classification-master/train/" # 未分词分类语料库路径
seg_path = "/home/appleyuchi/PycharmProjects/MultiNB/csdn_blog/54891204_tenwhy/chinese_text_classification-master/train_corpus_seg/" # 分词后分类语料库路径,本程序输出结果
corpus_segment(corpus_path,seg_path)
#对测试集进行分词
corpus_path = "/home/appleyuchi/PycharmProjects/MultiNB/csdn_blog/54891204_tenwhy/chinese_text_classification-master/answer/" # 未分词分类语料库路径
seg_path = "/home/appleyuchi/PycharmProjects/MultiNB/csdn_blog/54891204_tenwhy/chinese_text_classification-master/test_corpus_seg/" # 分词后分类语料库路径，本程序输出结果
corpus_segment(corpus_path,seg_path)

2.py(corpus2Bunch.py)

[python] view plain copy

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
@version: python2.7.8
@author: XiangguoSun
@contact: sunxiangguodut@qq.com
@file: corpus2Bunch.py
@time: 2017/2/7 7:41
@software: PyCharm
"""
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
import os#python内置的包，用于进行文件目录操作，我们将会用到os.listdir函数
import cPickle as pickle#导入cPickle包并且取一个别名pickle
'''''
事实上python中还有一个也叫作pickle的包，与这里的名字相同了，无所谓
关于cPickle与pickle，请参考博主另一篇博文：
python核心模块之pickle和cPickle讲解
http://blog.csdn.net/github_36326955/article/details/54882506
本文件代码下面会用到cPickle中的函数cPickle.dump
'''
from sklearn.datasets.base import Bunch
#这个您无需做过多了解，您只需要记住以后导入Bunch数据结构就像这样就可以了。
#今后的博文会对sklearn做更有针对性的讲解
def _readfile(path):
'''''读取文件'''
#函数名前面带一个_,是标识私有函数
# 仅仅用于标明而已，不起什么作用，
# 外面想调用还是可以调用，
# 只是增强了程序的可读性
with open(path, "rb") as fp:#with as句法前面的代码已经多次介绍过，今后不再注释
content = fp.read()
return content
def corpus2Bunch(wordbag_path,seg_path):
catelist = os.listdir(seg_path)# 获取seg_path下的所有子目录，也就是分类信息
#创建一个Bunch实例
bunch = Bunch(target_name=[], label=[], filenames=[], contents=[])
bunch.target_name.extend(catelist)
'''''
extend(addlist)是python list中的函数，意思是用新的list（addlist）去扩充
原来的list
'''
# 获取每个目录下所有的文件
for mydir in catelist:
class_path = seg_path + mydir + "/" # 拼出分类子目录的路径
file_list = os.listdir(class_path) # 获取class_path下的所有文件
for file_path in file_list: # 遍历类别目录下文件
fullname = class_path + file_path # 拼出文件名全路径
bunch.label.append(mydir)
bunch.filenames.append(fullname)
bunch.contents.append(_readfile(fullname)) # 读取文件内容
'''''append(element)是python list中的函数，意思是向原来的list中添加element，注意与extend()函数的区别'''
# 将bunch存储到wordbag_path路径中
with open(wordbag_path, "wb") as file_obj:
pickle.dump(bunch, file_obj)
print "构建文本对象结束！！！"
if __name__ == "__main__":#这个语句前面的代码已经介绍过，今后不再注释
#对训练集进行Bunch化操作：
wordbag_path = "/home/appleyuchi/PycharmProjects/MultiNB/csdn_blog/54891204_tenwhy/chinese_text_classification-master/train_word_bag/train_set.dat" # Bunch存储路径，程序输出
seg_path = "/home/appleyuchi/PycharmProjects/MultiNB/csdn_blog/54891204_tenwhy/chinese_text_classification-master/train_corpus_seg/" # 分词后分类语料库路径，程序输入
corpus2Bunch(wordbag_path, seg_path)
# 对测试集进行Bunch化操作：
wordbag_path = "/home/appleyuchi/PycharmProjects/MultiNB/csdn_blog/54891204_tenwhy/chinese_text_classification-master/test_word_bag/test_set.dat" # Bunch存储路径，程序输出
seg_path = "/home/appleyuchi/PycharmProjects/MultiNB/csdn_blog/54891204_tenwhy/chinese_text_classification-master/test_corpus_seg/" # 分词后分类语料库路径，程序输入
corpus2Bunch(wordbag_path, seg_path)

3.py(TFIDF_space.py)

[python] view plain copy

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
@version: python2.7.8
@author: XiangguoSun
@contact: sunxiangguodut@qq.com
@file: TFIDF_space.py
@time: 2017/2/8 11:39
@software: PyCharm
"""
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
from sklearn.datasets.base import Bunch
import cPickle as pickle
from sklearn.feature_extraction.text import TfidfVectorizer
def _readfile(path):
with open(path, "rb") as fp:
content = fp.read()
return content
def _readbunchobj(path):
with open(path, "rb") as file_obj:
bunch = pickle.load(file_obj)
return bunch
def _writebunchobj(path, bunchobj):
with open(path, "wb") as file_obj:
pickle.dump(bunchobj, file_obj)
def vector_space(stopword_path,bunch_path,space_path,train_tfidf_path=None):
stpwrdlst = _readfile(stopword_path).splitlines()
bunch = _readbunchobj(bunch_path)
tfidfspace = Bunch(target_name=bunch.target_name, label=bunch.label, filenames=bunch.filenames, tdm=[], vocabulary={})
if train_tfidf_path is not None:
trainbunch = _readbunchobj(train_tfidf_path)
tfidfspace.vocabulary = trainbunch.vocabulary
vectorizer = TfidfVectorizer(stop_words=stpwrdlst, sublinear_tf=True, max_df=0.5,vocabulary=trainbunch.vocabulary)
tfidfspace.tdm = vectorizer.fit_transform(bunch.contents)
else:
vectorizer = TfidfVectorizer(stop_words=stpwrdlst, sublinear_tf=True, max_df=0.5)
tfidfspace.tdm = vectorizer.fit_transform(bunch.contents)
tfidfspace.vocabulary = vectorizer.vocabulary_
_writebunchobj(space_path, tfidfspace)
print "tf-idf词向量空间实例创建成功！！！"
if __name__ == '__main__':
# stopword_path = "/home/appleyuchi/PycharmProjects/MultiNB/csdn_blog/54891204/chinese_text_classification-master/train_word_bag/hlt_stop_words.txt"#输入的文件
# bunch_path = "train_word_bag/train_set.dat"#输入的文件
# space_path = "train_word_bag/tfdifspace.dat"#输出的文件
# vector_space(stopword_path,bunch_path,space_path)
#
# bunch_path = "test_word_bag/test_set.dat"#输入的文件
# space_path = "test_word_bag/testspace.dat"
# train_tfidf_path="train_word_bag/tfdifspace.dat"
# vector_space(stopword_path,bunch_path,space_path,train_tfidf_path)
stopword_path = "/home/appleyuchi/PycharmProjects/MultiNB/csdn_blog/54891204_tenwhy/chinese_text_classification-master/train_word_bag/hlt_stop_words.txt"#输入的文件
train_bunch_path = "/home/appleyuchi/PycharmProjects/MultiNB/csdn_blog/54891204_tenwhy/chinese_text_classification-master/train_word_bag/train_set.dat"#输入的文件
space_path = "/home/appleyuchi/PycharmProjects/MultiNB/csdn_blog/54891204_tenwhy/chinese_text_classification-master/train_word_bag/tfidfspace.dat"#输出的文件
vector_space(stopword_path,train_bunch_path,space_path)
train_tfidf_path = "/home/appleyuchi/PycharmProjects/MultiNB/csdn_blog/54891204_tenwhy/chinese_text_classification-master/train_word_bag/tfidfspace.dat" # 输入的文件，由上面生成
test_bunch_path = "/home/appleyuchi/PycharmProjects/MultiNB/csdn_blog/54891204_tenwhy/chinese_text_classification-master/test_word_bag/test_set.dat"#输入的文件
test_space_path = "/home/appleyuchi/PycharmProjects/MultiNB/csdn_blog/54891204_tenwhy/chinese_text_classification-master/test_word_bag/testspace.dat"#输出的文件
vector_space(stopword_path,test_bunch_path,test_space_path,train_tfidf_path)

4.py

#!/usr/bin/env python
# -*- coding: UTF-8 -*-  import sys
reload(sys)
sys.setdefaultencoding('utf-8')  import cPickle as pickle
from sklearn.naive_bayes import MultinomialNB  # 导入多项式贝叶斯算法  # 读取bunch对象
def _readbunchobj(path):  with open(path, "rb") as file_obj:  bunch = pickle.load(file_obj)  return bunch  # 导入训练集
trainpath = "../train_word_bag/tfidfspace.dat"
train_set = _readbunchobj(trainpath)  # 导入测试集
testpath = "../test_word_bag/testspace.dat"
test_set = _readbunchobj(testpath)  # 训练分类器：输入词袋向量和分类标签，alpha:0.001 alpha越小，迭代次数越多，精度越高
# clf = MultinomialNB(alpha=0.1).fit(train_set.tdm, train_set.label)  ######################################################  from sklearn import tree
print '*************************决策树************************'  clf = tree.DecisionTreeClassifier()
clf.fit(train_set.tdm, train_set.label)  # 预测分类结果  print '*************************开始预测************************'
predicted = clf.predict(test_set.tdm)  for flabel,file_name,expct_cate in zip(test_set.label,test_set.filenames,predicted):  if flabel != expct_cate:  print file_name,": 实际类别:",flabel," -->预测类别:",expct_cate  print "预测完毕!!!"  # 计算分类精度：
from sklearn import metrics
def metrics_result(actual, predict):  print '精度:{0:.3f}'.format(metrics.precision_score(actual, predict,average='weighted'))  print '召回:{0:0.3f}'.format(metrics.recall_score(actual, predict,average='weighted'))  print 'f1-score:{0:.3f}'.format(metrics.f1_score(actual, predict,average='weighted'))  metrics_result(test_set.label, predicted)

依然使用复旦大学的新闻数据集

运行结果（这里复制一部分）：

../test_corpus_seg/C37-Military/C37-Military008.txt : 实际类别: C37-Military -->预测类别: C11-Space
../test_corpus_seg/C37-Military/C37-Military031.txt : 实际类别: C37-Military -->预测类别: C38-Politics
../test_corpus_seg/C37-Military/C37-Military101.txt : 实际类别: C37-Military -->预测类别: C38-Politics
../test_corpus_seg/C37-Military/C37-Military006.txt : 实际类别: C37-Military -->预测类别: C32-Agriculture
../test_corpus_seg/C37-Military/C37-Military125.txt : 实际类别: C37-Military -->预测类别: C29-Transport
预测完毕!!!
精度:0.878
召回:0.879
f1-score:0.878

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