1、朴素贝叶斯实现新闻分类的步骤
（1）提供文本文件，即数据集下载
（2）准备数据
将数据集划分为训练集和测试集；使用jieba模块进行分词，词频统计，停用词过滤，文本特征提取，将文本数据向量化
停用词文本stopwords_cn.txt下载
jieba模块学习：https://github.com/fxsjy/jieba ； https://www.oschina.net/p/jieba
（3）分析数据：使用matplotlib模块分析
（4）训练算法：使用sklearn.naive_bayes 的MultinomialNB进行训练
在scikit-learn中，一共有3个朴素贝叶斯的分类算法类。分别是GaussianNB，MultinomialNB和BernoulliNB。
其中GaussianNB就是先验为高斯分布的朴素贝叶斯，MultinomialNB就是先验为多项式分布的朴素贝叶斯，而BernoulliNB就是先验为伯努利分布的朴素贝叶斯。
（5）测试算法：使用测试集对贝叶斯分类器进行测试
2、代码实现
# -*- coding: UTF-8 -*-
import os
import random
import jieba
from sklearn.naive_bayes import MultinomialNB
import matplotlib.pyplot as plt
"""
函数说明:中文文本处理
Parameters:
folder_path - 文本存放的路径
test_size - 测试集占比，默认占所有数据集的百分之20
Returns:
all_words_list - 按词频降序排序的训练集列表
train_data_list - 训练集列表
test_data_list - 测试集列表
train_class_list - 训练集标签列表
test_class_list - 测试集标签列表
"""
def TextProcessing(folder_path, test_size=0.2):
folder_list = os.listdir(folder_path) # 查看folder_path下的文件
data_list = [] # 数据集数据
class_list = [] # 数据集类别
# 遍历每个子文件夹
for folder in folder_list:
new_folder_path = os.path.join(folder_path, folder) # 根据子文件夹，生成新的路径
files = os.listdir(new_folder_path) # 存放子文件夹下的txt文件的列表
j = 1
# 遍历每个txt文件
for file in files:
if j > 100: # 每类txt样本数最多100个
break
with open(os.path.join(new_folder_path, file), 'r', encoding='utf-8') as f: # 打开txt文件
raw = f.read()
word_cut = jieba.cut(raw, cut_all=False) # 精简模式，返回一个可迭代的generator
word_list = list(word_cut) # generator转换为list
data_list.append(word_list) # 添加数据集数据
class_list.append(folder) # 添加数据集类别
j += 1
data_class_list = list(zip(data_list, class_list)) # zip压缩合并，将数据与标签对应压缩
random.shuffle(data_class_list) # 将data_class_list乱序
index = int(len(data_class_list) * test_size) + 1 # 训练集和测试集切分的索引值
train_list = data_class_list[index:] # 训练集
test_list = data_class_list[:index] # 测试集
train_data_list, train_class_list = zip(*train_list) # 训练集解压缩
test_data_list, test_class_list = zip(*test_list) # 测试集解压缩
all_words_dict = {} # 统计训练集词频
for word_list in train_data_list:
for word in word_list:
if word in all_words_dict.keys():
all_words_dict[word] += 1
else:
all_words_dict[word] = 1
# 根据键的值倒序排序
all_words_tuple_list = sorted(all_words_dict.items(), key=lambda f: f[1], reverse=True)
all_words_list, all_words_nums = zip(*all_words_tuple_list) # 解压缩
all_words_list = list(all_words_list) # 转换成列表
return all_words_list, train_data_list, test_data_list, train_class_list, test_class_list
"""
函数说明:读取文件里的内容，并去重
Parameters:
words_file - 文件路径
Returns:
words_set - 读取的内容的set集合
"""
def MakeWordsSet(words_file):
words_set = set() # 创建set集合
with open(words_file, 'r', encoding='utf-8') as f: # 打开文件
for line in f.readlines(): # 一行一行读取
word = line.strip() # 去回车
if len(word) > 0: # 有文本，则添加到words_set中
words_set.add(word)
return words_set # 返回处理结果
"""
函数说明:文本特征选取
Parameters:
all_words_list - 训练集所有文本列表
deleteN - 删除词频最高的deleteN个词
stopwords_set - 指定的结束语
Returns:
feature_words - 特征集
"""
def words_dict(all_words_list, deleteN, stopwords_set=set()):
feature_words = [] # 特征列表
n = 1
for t in range(deleteN, len(all_words_list), 1):
if n > 1000: # feature_words的维度为1000
break
# 如果这个词不是数字，并且不是指定的结束语，并且单词长度大于1小于5，那么这个词就可以作为特征词
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
"""
函数说明:根据feature_words将文本向量化
Parameters:
train_data_list - 训练集
test_data_list - 测试集
feature_words - 特征集
Returns:
train_feature_list - 训练集向量化列表
test_feature_list - 测试集向量化列表
"""
def TextFeatures(train_data_list, test_data_list, feature_words):
def text_features(text, feature_words): # 出现在特征集中，则置1
text_words = set(text)
features = [1 if word in text_words else 0 for word in feature_words]
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 # 返回结果
"""
函数说明:新闻分类器
Parameters:
train_feature_list - 训练集向量化的特征文本
test_feature_list - 测试集向量化的特征文本
train_class_list - 训练集分类标签
test_class_list - 测试集分类标签
Returns:
test_accuracy - 分类器精度
"""
def TextClassifier(train_feature_list, test_feature_list, train_class_list, test_class_list):
classifier = MultinomialNB().fit(train_feature_list, train_class_list)
test_accuracy = classifier.score(test_feature_list, test_class_list)
return test_accuracy
if __name__ == '__main__':
# 文本预处理
folder_path = './SogouC/Sample' # 训练集存放地址
all_words_list, train_data_list, test_data_list, train_class_list, test_class_list = TextProcessing(folder_path,test_size=0.2)
# 生成stopwords_set
stopwords_file = './stopwords_cn.txt'
stopwords_set = MakeWordsSet(stopwords_file)
test_accuracy_list = []
"""
deleteNs = range(0, 1000, 20) # 0 20 40 60 ... 980
for deleteN in deleteNs:
feature_words = words_dict(all_words_list, deleteN, stopwords_set)
train_feature_list, test_feature_list = TextFeatures(train_data_list, test_data_list, feature_words)
test_accuracy = TextClassifier(train_feature_list, test_feature_list, train_class_list, test_class_list)
test_accuracy_list.append(test_accuracy)
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()
"""
feature_words = words_dict(all_words_list, 450, stopwords_set)
train_feature_list, test_feature_list = TextFeatures(train_data_list, test_data_list, feature_words)
test_accuracy = TextClassifier(train_feature_list, test_feature_list, train_class_list, test_class_list)
test_accuracy_list.append(test_accuracy)
ave = lambda c: sum(c) / len(c)
print(ave(test_accuracy_list))
结果为：
