機器學習實戰 樸素貝葉斯
阿新 • • 發佈：2019-02-26
樸素貝葉斯 amt 生成 文本 訓練 ini ror rds 詞向量
樸素貝葉斯
樸素貝葉斯概述
文本分類
準備數據:從文-本中構建詞向量
-訓練算法：從詞向量計算概率
-貝葉斯分類函數
import numpy as np import matplotlib.pyplot as plt from numpy import * """ function: 創建數據集 parameters: 無 returns: postingList - 數據集 classVec - 標簽集 """ def loadDataSet(): 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] return postingList, classVec """ function: 從數據集中提取詞匯表(不重復) parameters: dataSet - 數據集 retunrns: vocalSet - 不重復的詞匯表 """ def createVocabList(dataSet): vocabSet = set([]) for document in dataSet: vocabSet = vocabSet | set(document) return list(vocabSet) """ function: 根據之前創建的詞匯表來對輸入數據進行向量化 parameters: vocabList - 詞匯表 inputSet - 輸入的一個文檔 returns: returnVec - 該文檔向量 """ def setOfWords2Vec(vocabList, inputSet): returnVec = [0]*len(vocabList) for word in inputSet: if word in vocabList: returnVec[vocabList.index(word)] = 1 else:#前面創建詞匯表有問題 print("the word: %s is not in my Vocalbulary" % word) return returnVec """ function: 樸素貝葉斯分類器訓練函數 parameters: trainMatrix - 訓練文檔矩陣,每篇文檔調用set0OfWord2Vec生成的returnVec組成的矩陣 trainCategory - 標簽向量 returns: p0Vec - 侮辱類的條件概率數組，即每個詞匯屬於侮辱類的概率 p1Vect - 非侮辱類的條件概率數組，即每個詞匯屬於非侮辱類的概率 pAbusive - 文檔屬於侮辱類的概率 """ def trainNB0(trainMatrix, trainCategory): numTrainDocs = len(trainMatrix)#文檔數目 numWords = len(trainMatrix[0])#每篇文檔的詞條數 pAbusive = sum(trainCategory)/float(numTrainDocs)#文檔屬於侮辱類的概率 #詞條出現數初始化 p0Num = ones(numWords) p1Num = ones(numWords) #分母初始化 p0Denom = 2.0 p1Denom = 2.0 for i in range(numTrainDocs):#對每篇文檔 if trainCategory[i] == 1:#統計侮辱類的條件概率 p1Num += trainMatrix[i] p1Denom += sum(trainMatrix[i])#這裏我是理解成全概率公式 else: p0Num += trainMatrix[i] p0Denom += sum(trainMatrix[i]) p0Vect = log(p0Num/p0Denom) p1Vect = log(p1Num/p1Denom) return p0Vect, p1Vect, pAbusive """ function: 樸素貝葉斯分類函數 parameters: vec2Classify - 待分類向量 p0Vec - 侮辱類的條件概率數組 p1Vec - 非侮辱類的條件概率數組 pClass1 - 文檔屬於侮辱類的概率 returns: """ def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1): #對數相加，相當與計算兩個向量相乘的結果 #這裏不太懂，感覺和公式對不上 p1 = sum(vec2Classify * p1Vec) + log(pClass1) p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1) if p1 > p0: return 1 else: return 0 if __name__ == "__main__": listOPosts, listClasses = loadDataSet() myVocabList = createVocabList(listOPosts) trainMat = [] for postinDoc in listOPosts: trainMat.append(setOfWords2Vec(myVocabList, postinDoc)) p0V, p1V, pAb = trainNB0(array(trainMat), array(listClasses)) testEntry = ['love', 'my', 'dalmation'] thisDoc = array(setOfWords2Vec(myVocabList, testEntry)) print(testEntry," classified as: ",classifyNB(thisDoc,p0V,p1V,pAb)) testEntry = ['stupid', 'garbage'] thisDoc = array(setOfWords2Vec(myVocabList,testEntry)) print(testEntry," classified as: ",classifyNB(thisDoc,p0V,p1V,pAb))
詞袋模型
一個小優化，相比與之前只統計詞出現與否的詞條模型，詞袋模型統計詞出現的次數
-垃圾郵件過濾
不清楚為什麽我做出來的錯誤率這麽高，算了，先放著吧
import re import random from array import * import numpy as np from numpy import * """ function: 從數據集中提取詞匯表(不重復) parameters: dataSet - 數據集 retunrns: vocalSet - 不重復的詞匯表 """ def createVocabList(dataSet): vocabSet = set([]) for document in dataSet: vocabSet = vocabSet | set(document) return list(vocabSet) """ function: 根據之前創建的詞匯表來對輸入數據進行向量化 parameters: vocabList - 詞匯表 inputSet - 輸入的一個文檔 returns: returnVec - 該文檔向量 """ def setOfWords2Vec(vocabList, inputSet): returnVec = [0]*len(vocabList) for word in inputSet: if word in vocabList: returnVec[vocabList.index(word)] = 1 else:#前面創建詞匯表有問題 print("the word: %s is not in my Vocalbulary" % word) return returnVec """ function: 樸素貝葉斯分類器訓練函數 parameters: trainMatrix - 訓練文檔矩陣,每篇文檔調用set0OfWord2Vec生成的returnVec組成的矩陣 trainCategory - 標簽向量 returns: p0Vec - 侮辱類的條件概率數組，即每個詞匯屬於侮辱類的概率 p1Vect - 非侮辱類的條件概率數組，即每個詞匯屬於非侮辱類的概率 pAbusive - 文檔屬於侮辱類的概率 """ def trainNB0(trainMatrix, trainCategory): numTrainDocs = len(trainMatrix)#文檔數目 numWords = len(trainMatrix[0])#每篇文檔的詞條數 pAbusive = sum(trainCategory)/float(numTrainDocs)#文檔屬於侮辱類的概率 #詞條出現數初始化 p0Num = ones(numWords) p1Num = ones(numWords) #分母初始化 p0Denom = 2.0 p1Denom = 2.0 for i in range(numTrainDocs):#對每篇文檔 if trainCategory[i] == 1:#統計侮辱類的條件概率 p1Num += trainMatrix[i] p1Denom += sum(trainMatrix[i])#這裏我是理解成全概率公式 else: p0Num += trainMatrix[i] p0Denom += sum(trainMatrix[i]) p0Vect = log(p0Num/p0Denom) p1Vect = log(p1Num/p1Denom) return p0Vect, p1Vect, pAbusive """ function: 樸素貝葉斯分類函數 parameters: vec2Classify - 待分類向量 p0Vec - 侮辱類的條件概率數組 p1Vec - 非侮辱類的條件概率數組 pClass1 - 文檔屬於侮辱類的概率 returns: """ def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1): #對數相加，相當與計算兩個向量相乘的結果 #這裏不太懂，感覺和公式對不上 p1 = sum(vec2Classify * p1Vec) + log(pClass1) p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1) if p1 > p0: return 1 else: return 0 """ function: 處理文本 parameter: bigString - 文本 return: tok - 文本處理後所得詞匯向量 """ def textParse(bigString): listOfTokens = re.split(rb'\w*', bigString) return [tok.lower() for tok in listOfTokens if len(tok) > 2] """ function: 垃圾郵件過濾 parameter: 無 returns: 無 """ def spamTest(): docList = [] #數據集 classList = [] #標簽集 fullText = [] #??? for i in range(1,26): wordList = textParse(open('spam/%d.txt' % i, 'rb').read()) docList.append(wordList) fullText.extend(wordList) classList.append(1) wordList = textParse(open('ham/%d.txt' % i, 'rb').read()) docList.append(wordList) fullText.extend(wordList) classList.append(0) vocabList = createVocabList(docList)#創建詞匯表 trainingSet = list(range(50))#訓練集 testSet = []#測試集 for i in range(10):#選取10個測試集，並從訓練集中刪除 randIndex = int(random.uniform(0,len(trainingSet))) testSet.append(trainingSet[randIndex]) del(trainingSet[randIndex])#??? trainMat = []#數字化的訓練集 trainClasses = []#標簽集 for docIndex in trainingSet:#遍歷訓練集,計算數據 trainMat.append(setOfWords2Vec(vocabList, docList[docIndex])) trainClasses.append(classList[docIndex]) p0V, p1V, pSpam = trainNB0(array(trainMat), array(trainClasses)) errorCount = 0 for docIndex in testSet:#測試集計算錯誤率 wordVector= setOfWords2Vec(vocabList, docList[docIndex]) if classifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]: errorCount += 1 print("the error rate is: ", float(errorCount)/len(testSet)) if __name__ == "__main__": spamTest()
最後一個不寫了
機器學習實戰 樸素貝葉斯