NLP（四）：朴素贝叶斯原理及文本分类的sklearn实现

目录

1.朴素贝叶斯原理

2.基于的朴素贝叶斯的文本分类的sklearn实现

2.1首先基于sklearn的dataset数据集，贴上朴素贝叶斯手写数字识别的历程。

2.2sklearn朴素贝贝叶斯文本分类的实现

---

1.朴素贝叶斯原理

直接贴上自己的朴素贝叶斯（参考书籍为西瓜书）学习笔记：

---

2.基于的朴素贝叶斯的文本分类的sklearn实现

2.1首先基于sklearn的dataset数据集，贴上朴素贝叶斯手写数字识别的历程。

# @Author  : wpf
# @Email   : [email protected]
# @File    : 12.py
# @Software: PyCharm Community Edition

from sklearn import datasets, model_selection, naive_bayes
import matplotlib.pyplot as plt


# 可视化手写识别数据集Digit Dataset
def show_digits():
    digits = datasets.load_digits()
    fig = plt.figure()
    for i in range(20):
        ax = fig.add_subplot(4, 5, i + 1)
        ax.imshow(digits.images[i], cmap=plt.cm.gray_r, interpolation='nearest')
    plt.show()


show_digits()


# 加载Digit数据集
def load_data():
    digits = datasets.load_digits()
    return model_selection.train_test_split(digits.data, digits.target,
                                             test_size=0.25, random_state=0)


def test_GaussianNB(*data):
    X_train, X_test, y_train, y_test = data
    cls = naive_bayes.GaussianNB()
    cls.fit(X_train, y_train)
    print('GaussianNB Classifier')
    print('Training Score: %.2f' % cls.score(X_train, y_train))
    print('Test Score: %.2f' % cls.score(X_test, y_test))
    print(X_test)
    pre = cls.predict(X_test)
    print(pre)

X_train, X_test, y_train, y_test = load_data()
print(test_GaussianNB(X_train, X_test, y_train, y_test))


def test_MultinomialNB(*data):
    X_train, X_test, y_train, y_test = data
    cls = naive_bayes.MultinomialNB()
    cls.fit(X_train, y_train)
    print('MultinomialNB Classifier')
    print('Training Score: %.2f' % cls.score(X_train, y_train))
    print('Test Score: %.2f' % cls.score(X_test, y_test))


X_train, X_test, y_train, y_test = load_data()
print(test_MultinomialNB(X_train, X_test, y_train, y_test))


def test_BernoulliNB(*data):
    X_train, X_test, y_train, y_test = data
    cls = naive_bayes.BernoulliNB()
    cls.fit(X_train, y_train)
    print('BernoulliNB Classifier')
    print('Training Score: %.2f' % cls.score(X_train, y_train))
    print('Test Score: %.2f' % cls.score(X_test, y_test))


X_train, X_test, y_train, y_test = load_data()
print(test_BernoulliNB(X_train, X_test, y_train, y_test))

2.2sklearn朴素贝贝叶斯文本分类的实现

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


# 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), 'rb') as fp:
                text = 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(text, 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 #list里套list,每个list是没篇文章分完词的Unicode编码
            class_list.append(folder)  # 类别，即文件夹的名字（代表类别）
            j += 1

    ## 粗暴地划分训练集和测试集
    data_class_list = list(zip(data_list, class_list))  # 将列表对应元素合并，创建一个元组队的列表
    random.shuffle(data_class_list)
    index = int(len(data_class_list) * test_size) + 1  # 20% 当做训练集，index为19
    train_list = data_class_list[index:]  # 19-89共71个设为训练集
    test_list = data_class_list[:index]  # 0-18共19个设为测试集
    train_data_list, train_class_list = zip(*train_list)  # 将映射的元组unzip成原来的两个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 = {}  # 字典类型
    for word_list in train_data_list:
        for word in word_list:  # 遍历训练集中每个词语
            if word in all_words_dict:
                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]  # 将元组分成两个list，只要第一个list,all_words_list为训练集中所有词语按词频排序的list

    return all_words_list, train_data_list, test_data_list, train_class_list, test_class_list


#粗暴的词统计
def make_word_set(words_file):
    words_set = set()#set类型没有重复的元素
    with open(words_file, 'rb') as fp:
        for line in fp.readlines():#按行读，每行是一个停用词
            word = line.strip().decode("utf-8")#去除首位空格
            if len(word)>0 and word not in words_set: # 去重
                words_set.add(word)
    return words_set


# 选取特征词
#去掉停用词，和词频高的（比较normal）的词，得到说有的特征词集，构成词袋
def words_dict(all_words_list, deleteN, stopwords_set=set()):
    feature_words = []
    n = 1
    for t in range(deleteN, len(all_words_list), 1):#去掉词频较高的前deleteN个词，可能是停用词或比较normal的词
        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