朴素贝叶斯进行新闻主题分类，有代码和数据，可以跑通

folder_path = '/Users/apple/Documents/七月在线/NLP/第2课/Lecture_2/Naive-Bayes-Text-Classifier/Database/SogouC/Sample'
stopwords_file = '/Users/apple/Documents/七月在线/NLP/第2课/Lecture_2/Naive-Bayes-Text-Classifier/stopwords_cn.txt'
 

下载地址：链接:https://pan.baidu.com/s/1O5mW04PlulaCW5TUd93OUg  密码:ubkq

然后切换Python2.7，跑下面代码就可以进行自然语言入门了

#coding: utf-8
#python 2.7 运行正确

'''
 经典的新闻主题分类，用朴素贝叶斯做。

#2018-06-10  June Sunday the 23 week, the 161 day SZ
数据来源：链接:https://pan.baidu.com/s/1_w7wOzNkUEaq3KAGco19EQ 密码:87o0
朴素贝叶斯与应用
文本分类问题
经典的新闻主题分类，用朴素贝叶斯做。


朴素贝叶斯进行文本分类的基本思路是先区分好训练集与测试集，对文本集合进行分词、去除标点符号等特征预处理的操作，然后使用条件独立假设， 将原概率转换成词概率乘积，再进行后续的处理。 贝叶斯公式 + 条件独立假设 = 朴素贝叶斯方法 基于对重复词语在训练阶段与判断（测试）阶段的三种不同处理方式，我们相应的有伯努利模型、多项式模型和混合模型。 在训练阶段，如果样本集合太小导致某些词语并未出现，我们可以采用平滑技术对其概率给一个估计值。 而且并不是所有的词语都需要统计，我们可以按相应的“停用词”和“关键词”对模型进行进一步简化，提高训练和判断速度。

'''

import os
import time
import random
import jieba  #处理中文
#import nltk  #处理英文
import sklearn
from sklearn.naive_bayes import MultinomialNB
import numpy as np
import pylab as pl
import matplotlib.pyplot as plt

#粗暴的词去重
def make_word_set(words_file):
    words_set = set()
    with open(words_file, 'r') as fp:
        for line in fp.readlines():
            word = line.strip().decode("utf-8")
            if len(word)>0 and word not in words_set: # 去重
                words_set.add(word)
    return words_set

# 文本处理，也就是样本生成过程
def text_processing(folder_path, test_size=0.2):
    folder_list = os.listdir(folder_path)
    data_list = []
    class_list = []

    # 遍历文件夹
    for folder in folder_list:
        new_folder_path = os.path.join(folder_path, folder)
        files = os.listdir(new_folder_path)
        # 读取文件
        j = 1
        for file in files:
            if j > 100: # 怕内存爆掉，只取100个样本文件，你可以注释掉取完
                break
            with open(os.path.join(new_folder_path, file), 'r') as fp:
               raw = fp.read()
            ## 是的，随处可见的jieba中文分词
            jieba.enable_parallel(4) # 开启并行分词模式，参数为并行进程数，不支持windows
            word_cut = jieba.cut(raw, cut_all=False) # 精确模式，返回的结构是一个可迭代的genertor
            word_list = list(word_cut) # genertor转化为list，每个词unicode格式
            jieba.disable_parallel() # 关闭并行分词模式
            
            data_list.append(word_list) #训练集list
            class_list.append(folder.decode('utf-8')) #类别
            j += 1
    
    ## 粗暴地划分训练集和测试集
    data_class_list = zip(data_list, class_list)
    random.shuffle(data_class_list)
    index = int(len(data_class_list)*test_size)+1
    train_list = data_class_list[index:]
    test_list = data_class_list[:index]
    train_data_list, train_class_list = zip(*train_list)
    test_data_list, test_class_list = zip(*test_list)
    
    #其实可以用sklearn自带的部分做
    #train_data_list, test_data_list, train_class_list, test_class_list = sklearn.cross_validation.train_test_split(data_list, class_list, test_size=test_size)
    

    # 统计词频放入all_words_dict
    all_words_dict = {}
    for word_list in train_data_list:
        for word in word_list:
            if all_words_dict.has_key(word):
                all_words_dict[word] += 1
            else:
                all_words_dict[word] = 1

    # key函数利用词频进行降序排序
    all_words_tuple_list = sorted(all_words_dict.items(), key=lambda f:f[1], reverse=True) # 内建函数sorted参数需为list
    all_words_list = list(zip(*all_words_tuple_list)[0])

    return all_words_list, train_data_list, test_data_list, train_class_list, test_class_list


def words_dict(all_words_list, deleteN, stopwords_set=set()):
    # 选取特征词
    feature_words = []
    n = 1
    for t in range(deleteN, len(all_words_list), 1):
        if n > 1000: # feature_words的维度1000
            break
            
        if not all_words_list[t].isdigit() and all_words_list[t] not in stopwords_set and 1<len(all_words_list[t])<5:
            feature_words.append(all_words_list[t])
            n += 1
    return feature_words

# 文本特征
def text_features(train_data_list, test_data_list, feature_words, flag='nltk'):
    def text_features(text, feature_words):
        text_words = set(text)
        ## -----------------------------------------------------------------------------------
        if flag == 'nltk':
            ## nltk特征 dict
            features = {word:1 if word in text_words else 0 for word in feature_words}
        elif flag == 'sklearn':
            ## sklearn特征 list
            features = [1 if word in text_words else 0 for word in feature_words]
        else:
            features = []
        ## -----------------------------------------------------------------------------------
        return features
    train_feature_list = [text_features(text, feature_words) for text in train_data_list]
    test_feature_list = [text_features(text, feature_words) for text in test_data_list]
    return train_feature_list, test_feature_list

# 分类，同时输出准确率等
def text_classifier(train_feature_list, test_feature_list, train_class_list, test_class_list, flag='nltk'):
    ## -----------------------------------------------------------------------------------
    if flag == 'nltk':
        ## 使用nltk分类器
        train_flist = zip(train_feature_list, train_class_list)
        test_flist = zip(test_feature_list, test_class_list)
        classifier = nltk.classify.NaiveBayesClassifier.train(train_flist)
        test_accuracy = nltk.classify.accuracy(classifier, test_flist)
    elif flag == 'sklearn':
        ## sklearn分类器
        classifier = MultinomialNB().fit(train_feature_list, train_class_list)
        test_accuracy = classifier.score(test_feature_list, test_class_list)
    else:
        test_accuracy = []
    return test_accuracy

print "start"

## 文本预处理
folder_path = '/Users/apple/Documents/七月在线/NLP/第2课/Lecture_2/Naive-Bayes-Text-Classifier/Database/SogouC/Sample'
all_words_list, train_data_list, test_data_list, train_class_list, test_class_list = text_processing(folder_path,test_size=0.2)
 
# 生成stopwords_set
stopwords_file = '/Users/apple/Documents/七月在线/NLP/第2课/Lecture_2/Naive-Bayes-Text-Classifier/stopwords_cn.txt'
stopwords_set = make_word_set(stopwords_file)

## 文本特征提取和分类
# flag = 'nltk'
flag = 'sklearn'
deleteNs = range(0, 1000, 20)
test_accuracy_list = []
for deleteN in deleteNs:
    # feature_words = words_dict(all_words_list, deleteN)
    feature_words = words_dict(all_words_list, deleteN, stopwords_set)
    train_feature_list, test_feature_list = text_features(train_data_list, test_data_list, feature_words, flag)
    test_accuracy = text_classifier(train_feature_list, test_feature_list, train_class_list, test_class_list, flag)
    test_accuracy_list.append(test_accuracy)
print test_accuracy_list

# 结果评价
#plt.figure()
plt.plot(deleteNs, test_accuracy_list)
plt.title('Relationship of deleteNs and test_accuracy')
plt.xlabel('deleteNs')
plt.ylabel('test_accuracy')
plt.show()
#plt.savefig('result.png')

print "finished"

'''
输出
start
Building prefix dict from the default dictionary ...
Loading model from cache /var/folders/4k/5k587rhs73n7z73g0ssc81km0000gn/T/jieba.cache
Loading model cost 0.364 seconds.
Prefix dict has been built succesfully.
[0.5263157894736842, 0.5263157894736842, 0.47368421052631576, 0.47368421052631576, 0.47368421052631576, 0.47368421052631576, 0.5263157894736842, 0.5789473684210527, 0.5263157894736842, 0.5263157894736842, 0.5263157894736842, 0.5263157894736842, 0.47368421052631576, 0.47368421052631576, 0.47368421052631576, 0.47368421052631576, 0.47368421052631576, 0.47368421052631576, 0.5263157894736842, 0.5263157894736842, 0.5263157894736842, 0.5263157894736842, 0.5263157894736842, 0.5263157894736842, 0.5263157894736842, 0.5263157894736842, 0.5263157894736842, 0.47368421052631576, 0.42105263157894735, 0.42105263157894735, 0.42105263157894735, 0.42105263157894735, 0.3684210526315789, 0.3684210526315789, 0.3684210526315789, 0.3684210526315789, 0.3684210526315789, 0.3684210526315789, 0.42105263157894735, 0.42105263157894735, 0.42105263157894735, 0.47368421052631576, 0.42105263157894735, 0.47368421052631576, 0.47368421052631576, 0.47368421052631576, 0.42105263157894735, 0.47368421052631576, 0.47368421052631576, 0.5263157894736842]


'''

输出图像：

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