目录
1. 准备数据：从文本中构建词向量
1.1 词表到向量的转换函数
2. 训练算法：从词向量计算概率
3. 测试算法：根据现实情况修改分类器
3.1 朴素贝叶斯分类函数
4. 准备数据： 文档词袋模型
1. 准备数据：从文本中构建词向量
1.1 词表到向量的转换函数
def loaddataset(): # 创建一些实验样本postinglist = [['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],['stop', 'posting', 'stupid', 'worthless', 'garbage'],['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'],['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]classvec = [0, 1, 0, 1, 0, 1] # 1代表侮辱性文字，0代表正常言论return postinglist, classvec# postinglist：进行词条切分后的文档集合# classvec：类别标签的集合def createvocablist(dataset): # 会创建一个包含在所有文档中出现的不重复词的列表vocabset = set([]) # 创建一个空集for document in dataset:vocabset = vocabset | set(document) # 创建两个集合的并集return list(vocabset)def setofwords2vec(vocablist, inputset):# 输入参数为词汇表及某个文档returnvec = [0] * len(vocablist) # 创建一个和词汇表等长的向量，并将其元素都设为0for word in inputset: # 遍历文档中的所有单词if word in vocablist:returnvec[vocablist.index(word)] = 1# 如果出现了词汇表中的单词，则将输出的文档向量中的对应值设为1else:print("the word: %s is not in my Vocabulary!" % word)return returnvec# 输出的是文档向量，向量的每一元素为1或0# 分别表示词汇表中的单词在输入文档中是否出现
查看函数执行效果：
listOposts, listclasses = loaddataset()myvocablist = createvocablist(listOposts)print(myvocablist)
输出结果：
查看setofwords2vec（）的运行结果：
print(setofwords2vec(myvocablist, listOposts[0]))print(setofwords2vec(myvocablist, listOposts[3]))
输出结果：
2. 训练算法：从词向量计算概率
import numpy as npdef trainnb0(trainmatrix,traincategory):# trainmatrix: 文档矩阵；# traincategory：由每篇文档类别标签所构成的向量numtraindocs = len(trainmatrix)numwords = len(trainmatrix[0])pabusive = sum(traincategory)/float(numtraindocs)"下两行初始化分子变量和分母变量"p0num = np.zeros(numwords); p1num = np.zeros(numwords)p0denom = 0.0; p1denom = 0.0for i in range(numtraindocs):if traincategory[i] == 1:p1num += trainmatrix[i]p1denom += sum(trainmatrix[i])else:p0num += trainmatrix[i]p0denom += sum(trainmatrix[i])p1vect = p1num/p1denomp0vect = p0num/p0denomreturn p0vect,p1vect,pabusive
从预先加载值中调入数据：
listOposts, listclasses = loaddataset()
构建一个包含所有词的列表myvocablist：
myvocablist = createvocablist(listOposts)
for循环使用词向量来填充trainmat列表：
trainmat = []for postindoc in listOposts:trainmat.append(setofwords2vec(myvocablist, postindoc))
下面给出属于侮辱性文档的概率以及两个类别的概率向量：
p0v, p1v, pab = trainnb0(trainmat, listclasses)print("pab:", pab)print("属于侮辱性文档的概率：\n", p0v)print("属于侮辱性文档的概率：\n", p1v)
输出结果：
3. 测试算法：根据现实情况修改分类器
修改后的trainb0（）函数：
def trainnb0(trainmatrix, traincategory):# trainmatrix: 文档矩阵；# traincategory：由每篇文档类别标签所构成的向量numtraindocs = len(trainmatrix)numwords = len(trainmatrix[0])pabusive = sum(traincategory)/float(numtraindocs)"下两行初始化分子变量和分母变量"p0num = np.ones(numwords); p1num = np.ones(numwords)p0denom = 2.0; p1denom = 2.0for i in range(numtraindocs):if traincategory[i] == 1:p1num += trainmatrix[i]p1denom += sum(trainmatrix[i])else:p0num += trainmatrix[i]p0denom += sum(trainmatrix[i])p1vect = np.log(p1num/p1denom)p0vect = np.log(p0num/p0denom)return p0vect,p1vect,pabusive
3.1 朴素贝叶斯分类函数
def classifynb(vec2classify, p0vec, p1vec, pclass1):p1 = sum(vec2classify * p1vec) + np.log(pclass1) # element-wise multp0 = sum(vec2classify * p0vec) + np.log(1.0 - pclass1)if p1 > p0:return 1else:return 0def testingnb():listOposts, listclasses = loaddataset()myvocablist = createvocablist(listOposts)trainmat = []for postindoc in listOposts:trainmat.append(setofwords2vec(myvocablist, postindoc))p0v, p1v, pab = trainnb0(np.array(trainmat), np.array(listclasses))testentry = ['love', 'my', 'dalmation']thisdoc = np.array(setofwords2vec(myvocablist, testentry))print(testentry, 'classified as: ', classifynb(thisdoc, p0v, p1v, pab))testentry = ['stupid', 'garbage']thisdoc = np.array(setofwords2vec(myvocablist, testentry))print(testentry, 'classified as: ', classifynb(thisdoc, p0v, p1v, pab))
查看分类器输出结果：
testingnb()
输出结果：
4. 准备数据： 文档词袋模型
def bagofwords2Vecmn(vocablist, inputset):returnVec = [0] * len(vocablist)for word in inputset:if word in vocablist:returnVec[vocablist.index(word)] += 1return returnVec
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