Python實現樸素貝葉斯演算法

1、演算法介紹

樸素貝葉斯假設特徵相互獨立，於是有下面的公式： x表示特徵向量，c表示類別。

原理： 通過數據集計算出公式中右邊分子中的各個概率。預測時根據公式計算出該樣本屬於不同類別的概率，選擇概率大的那個類別作為預測值。

2、演算法流程

模型中各個概率的計算，看下面代碼中的 trainNB() 函數。

3、Python代碼實現及注釋

import numpy as np

# 創建數據集

def loadDataSet():

# 每個樣本為一段文字

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"]]

# 1表示不好，0表示好

classVec = [0, 1, 0, 1, 0, 1]

return postingList, classVec

# 從數據集中生成辭彙表

def createVocabList(dataSet):

# 創建空集合

vocabSet = set([])

# 提取數據集中所有的辭彙，不重複

for document in dataSet:

vocabSet = vocabSet | set(document)

return list(vocabSet)

# 把文本轉化為特徵向量

def setOfWords2Vec(vocabList, inputSet):

# 創建特徵向量，全0

returnVec = [0]*len(vocabList)

# 文本中出現的辭彙相應的在特徵向量中對應的位置置1

for word in inputSet:

if word in vocabList:

returnVec[vocabList.index(word)] = 1

else:

print("the word: %s is not in my Vocabulary!" % word)

return returnVec

# 創建貝葉斯分類器，輸入用特徵向量表示的數據集及數據集的標記

def trainNB(trainMatrix,trainCategory):

numTrainDocs = len(trainMatrix)

numWords = len(trainMatrix[0])

# 計算數據集中不好語句的佔比

pAbusive = sum(trainCategory)/float(numTrainDocs)

# 存放不同類別中各辭彙出現的次數

p0Num = np.ones(numWords)

p1Num = np.ones(numWords)

# 存放不同類別中辭彙的總數

p0Denom = 2.0

p1Denom = 2.0

# 遍曆數據集，計算上述四個值

for i in range(numTrainDocs):

if trainCategory[i] == 1:

p1Num += trainMatrix[i]

p1Denom += sum(trainMatrix[i])

else:

p0Num += trainMatrix[i]

p0Denom += sum(trainMatrix[i])

# 計算不同類別中各辭彙出現的概率

p1Vect = np.log(p1Num/p1Denom)

p0Vect = np.log(p0Num/p0Denom)

return p0Vect, p1Vect, pAbusive

# 用樸素的貝葉斯分類

def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):

# 計算該樣本屬於不同類別的概率，注意加了一個log

p1 = sum(vec2Classify * p1Vec) + np.log(pClass1)

p0 = sum(vec2Classify * p0Vec) + np.log(1.0 - pClass1)

# 根據概率預測結果

if p1 > p0:

return 1

else:

return 0

# 把數據集轉換為用特徵向量表示樣本的數據集

def getTrainMat(trainSet, myVocabList):

trainMat = []

for example in trainSet:

trainMat.append(setOfWords2Vec(myVocabList, example))

return trainMat

if __name__ == "__main__":

# 載入數據集

trainSet, labels = loadDataSet()

# 構建辭彙表

myVocabList = createVocabList(trainSet)

# 得到用特徵向量表示樣本的數據集

trainMat = getTrainMat(trainSet, myVocabList)

# 樸素的貝葉斯分類器得到模型參數

p0V, p1V, pAb = trainNB(trainMat, labels)

# print(p0V, "
", p1V, "
", pAb)

# 測試分類器

test = ["cute", "has"]

testVec = np.array(setOfWords2Vec(myVocabList, test))

predict = classifyNB(testVec, p0V, p1V, pAb)

print(test, "be predicted as ", predict)

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