决策树实现

#-*- coding=utf8 -*-import numpy as npfrom math import logdef createTree(dataSet,labels):    classList=[s[-1] for s in dataSet]    if classList.count(classList[0])==len(classList):        return classList[0]              #叶节点    elif len(dataSet[0])==1:        return majorityLabel(dataSet)   #叶节点    bestFeat=chooseBestFeature(dataSet)    # print labels    # print bestFeat    bestfeatlabel=labels[bestFeat]    myTree={bestfeatlabel:{}}    del(labels[bestFeat])    featvalues=[example[bestFeat] for example in dataSet]    uniqueValue=set(featvalues)    for value in uniqueValue:        subLabels = labels[:]  #这里必须写成subLabels = labels[:]而不能是subLabels = labels        myTree[bestfeatlabel][value]=createTree(splitDataSet(dataSet,bestFeat,value),subLabels)    return myTreedef splitDataSet(dataSet,feat,value):    subDateSet=[]    for line in dataSet:        if line[feat]==value:            lineSubData=line[:feat]            lineSubData.extend(line[feat+1:])            subDateSet.append(lineSubData)    return subDateSetdef majorityLabel(classList):    counts={}    unique=set(classList)    for i in unique:        counts[i]=classList.count(i)    sortedlabel = sorted(counts.iteritems(), key=lambda asd: asd[1], reverse=False)    return sortedlabel[0][0]def calcEntropy(dataSet):    classList=[example[-1] for example in dataSet]    numberData = len(classList)    uniqueLabel=set(classList)    counts=[]    for i in uniqueLabel:        counts.append(classList.count(i))    entropy=0.0    for i in counts:        prop=i*1.0/numberData        entropy-=prop*log(prop,2)    # print entropy    return entropydef chooseBestFeature(dataSet):    numFeatures=len(dataSet[0])-1    baseEntropy=calcEntropy(dataSet)    maxEntropy=0.0;bestFeature=-1    for i in range(numFeatures):        featureValues=[example[i] for example in dataSet]        uniqueValue=set(featureValues)        newEntropy=0.0        for value in uniqueValue:            subDataSet=splitDataSet(dataSet,i,value)            prob=len(subDataSet)/float(len(dataSet))            newEntropy+=prob*calcEntropy(subDataSet)        infoGain=baseEntropy-newEntropy        if infoGain>maxEntropy:            maxEntropy=infoGain            bestFeature=i    return bestFeaturedef storeTree(inputTree, filename):    import pickle    fw = open(filename, 'w')    pickle.dump(inputTree, fw)    fw.close()def grabTree(filename):    import pickle    fr = open(filename)    return pickle.load(fr)dataSet = [[1, 1, 'yes'],               [1, 1, 'yes'],               [1, 0, 'no'],               [0, 1, 'no'],               [0, 1, 'no']]labels = ['no surfacing','flippers']## storeTree(createTree(dataSet,labels),'mytree.txt')fr=open('Ch03\lenses.txt')lenses=[inst.strip().split('\t') for inst in fr.readlines()]lenseLabel=['age','pres','asti','tear','ui','iu']lensetree=createTree(lenses,lenseLabel)# grabTree('mytree.txt')print lensetree