一：训练模型、实现预测函数
import numpy as np import re import random def loadDataSet(): DataSetList=[] #全部数据集 classVec = [] #标签值 #获取数据 SpamPath = "email/spam/{}.txt" #获取文件路径 HamPath = "email/ham/{}.txt" for i in range(1,26): #两个路径各有25个文件 documentData = open(SpamPath.format(i),'r', encoding='ISO-8859-1').read() #使用正则进行分割，除了空格、还有标点都可以用于分割 WordVec = re.split(r"W+",documentData) #W*表示匹配多个非字母、数字、下划线的字符 DataSetList.append([item for item in WordVec if len(item)>0]) classVec.append(1) documentData = open(HamPath.format(i), 'r', encoding='ISO-8859-1').read() # 使用正则进行分割，除了空格、还有标点都可以用于分割 WordVec = re.split(r"W+", documentData) # W*表示匹配多个非字母、数字、下划线的字符 DataSetList.append([item for item in WordVec if len(item)>0]) classVec.append(0) return DataSetList,classVec def createVocabList(dataSet): #创建词汇列表 将多个文档单词归为一个 VocabSet = set([]) #使用集合，方便去重 for document in dataSet: VocabSet = VocabSet | set(document) #保持同类型 VocabList = list(VocabSet) VocabList.sort() return VocabList def WordSet2Vec(VocaList,inputSet): #根据我们上面得到的全部词汇列表，将我们输入得inputSet文档向量化 returnVec = [0]*len(VocaList) for word in inputSet: if word in VocaList: returnVec[VocaList.index(word)] = 1 else: print("the word: %s is not in my Vocabulary!"%word) return returnVec def trainNB0(trainMatrix,trainCategory): #训练朴素贝叶斯模型 传入numpy数组类型 trainMatrix所有文档词汇向量矩阵（m*n矩阵 m个文档，每个文档都是n列，代表词汇向量大小),trainCategory每篇文档得标签 numTrainDoc = len(trainMatrix) #文档数量 pC1 = np.sum(trainCategory)/numTrainDoc #p(c1)得概率 p(c0)=1-p(c1) wordVecNum = len(trainMatrix[0]) #因为每个文档转换为词汇向量后都是一样得长度 #初始化p1,p0概率向量---改进为拉普拉斯平滑 p1VecNum,p0VecNum = np.ones(wordVecNum),np.ones(wordVecNum) p1Sum,p0Sum = 2.0,2.0 #N*1 N表示分类数 #循环每一个文档 for i in range(numTrainDoc): if trainCategory[i] == 1: #侮辱性文档 p1VecNum += trainMatrix[i] #统计侮辱性文档中，每个单词出现频率 p1Sum += np.sum(trainMatrix[i]) #统计侮辱性文档中出现得全部单词数 每个单词出现概率就是单词出现频率/全部单词 else: #正常文档 p0VecNum += trainMatrix[i] p0Sum += np.sum(trainMatrix[i]) p1Vect = np.log(p1VecNum / p1Sum) #统计各类文档中的单词出现频率 p0Vect = np.log(p0VecNum / p0Sum) #使用对数避免下溢 return p1Vect,p0Vect,pC1 def classifyNB(testVec,p0Vec,p1Vec,pC1): p1 = sum(testVec*p1Vec)+np.log(pC1) #使用对数之后变为求和 p0 = sum(testVec*p0Vec)+np.log(1-pC1) if p1 > p0: return 1 else: return 0
二：实现K折交叉验证法---k=5
def OneCrossValidate(trainSet,trainCls,testSet,testCls): #训练模型 p1Vect,p0Vect,pC1 = trainNB0(np.array(trainSet),np.array(trainCls)) err_count = 0 #验证集进行测试 for i in range(10): c = classifyNB(np.array(testSet[i]),p0Vect,p1Vect,pC1) if c != testCls[i]: err_count += 1 return err_count/10 def KCrossValidate(trainMat,trainClassVec): #K折交叉验证 5 randIdx = list(range(50)) random.shuffle(randIdx) error_radio = 0.0 for i in range(5): #5次 rdInd = randIdx #随机索引 #选取训练集、验证集索引 trainSet = [] trainCls = [] testSet = [] testCls = [] testSetIdx = set(randIdx[10*i:10*i+10]) # 训练集 trainSetIdx = set(rdInd)-testSetIdx # 验证集 #选取训练集、验证集数据 for idx in trainSetIdx: trainSet.append(trainMat[idx]) trainCls.append(trainClassVec[idx]) for idx in testSetIdx: testSet.append(trainMat[idx]) testCls.append(trainClassVec[idx]) print(OneCrossValidate(trainSet,trainCls,testSet,testCls)) error_radio += OneCrossValidate(trainSet,trainCls,testSet,testCls) return error_radio/5 DocData,classVec = loadDataSet() voclist = createVocabList(DocData) #获取全部文档词汇向量矩阵 trainMulList = [] for doc in DocData: trainMulList.append(WordSet2Vec(voclist,doc)) print(KCrossValidate(trainMulList,classVec))
