tree.py

#coding:utf-8

from math import log
import operator
import treePlotter

def read_dataset(filename):
    """
    年龄段：0代表青年，1代表中年，2代表老年；
    有工作：0代表否，1代表是；
    有自己的房子：0代表否，1代表是；
    信贷情况：0代表一般，1代表好，2代表非常好；
    类别(是否给贷款)：0代表否，1代表是
    """
    fr=open(filename,'r')
    all_lines=fr.readlines()   #list形式,每行为1个str
    #print all_lines
    labels=['年龄段', '有工作', '有自己的房子', '信贷情况'] 
    #featname=all_lines[0].strip().split(',')  #list形式
    #featname=featname[:-1]
    labelCounts={}
    dataset=[]
    for line in all_lines[0:]:
        line=line.strip().split(',')   #以逗号为分割符拆分列表
        dataset.append(line)
    return dataset,labels

def read_testset(testfile):
    """
    年龄段：0代表青年，1代表中年，2代表老年；
    有工作：0代表否，1代表是；
    有自己的房子：0代表否，1代表是；
    信贷情况：0代表一般，1代表好，2代表非常好；
    类别(是否给贷款)：0代表否，1代表是
    """
    fr=open(testfile,'r')
    all_lines=fr.readlines()
    testset=[]
    for line in all_lines[0:]:
        line=line.strip().split(',')   #以逗号为分割符拆分列表
        testset.append(line)
    return testset

#计算信息熵
def jisuanEnt(dataset):
    numEntries=len(dataset)
    labelCounts={}
    #给所有可能分类创建字典
    for featVec in dataset:
        currentlabel=featVec[-1]
        if currentlabel not in labelCounts.keys():
            labelCounts[currentlabel]=0
        labelCounts[currentlabel]+=1
    Ent=0.0
    for key in labelCounts:
        p=float(labelCounts[key])/numEntries
        Ent=Ent-p*log(p,2)#以2为底求对数
    return Ent

#划分数据集
def splitdataset(dataset,axis,value):
    retdataset=[]#创建返回的数据集列表
    for featVec in dataset:#抽取符合划分特征的值
        if featVec[axis]==value:
            reducedfeatVec=featVec[:axis] #去掉axis特征
            reducedfeatVec.extend(featVec[axis+1:])#将符合条件的特征添加到返回的数据集列表
            retdataset.append(reducedfeatVec)
    return retdataset

'''
选择最好的数据集划分方式
ID3算法:以信息增益为准则选择划分属性
C4.5算法：使用“增益率”来选择划分属性
'''
#ID3算法
def ID3_chooseBestFeatureToSplit(dataset):
    numFeatures=len(dataset[0])-1
    baseEnt=jisuanEnt(dataset)
    bestInfoGain=0.0
    bestFeature=-1
    for i in range(numFeatures): #遍历所有特征
        #for example in dataset:
            #featList=example[i]  
        featList=[example[i]for example in dataset]
        uniqueVals=set(featList) #将特征列表创建成为set集合，元素不可重复。创建唯一的分类标签列表
        newEnt=0.0
        for value in uniqueVals:     #计算每种划分方式的信息熵
            subdataset=splitdataset(dataset,i,value)
            p=len(subdataset)/float(len(dataset))
            newEnt+=p*jisuanEnt(subdataset)
        infoGain=baseEnt-newEnt
        print(u"ID3中第%d个特征的信息增益为：%.3f"%(i,infoGain))
        if (infoGain>bestInfoGain):
            bestInfoGain=infoGain    #计算最好的信息增益
            bestFeature=i
    return bestFeature   

#C4.5算法
def C45_chooseBestFeatureToSplit(dataset):
    numFeatures=len(dataset[0])-1
    baseEnt=jisuanEnt(dataset)
    bestInfoGain_ratio=0.0
    bestFeature=-1
    for i in range(numFeatures): #遍历所有特征
        featList=[example[i]for example in dataset]  
        uniqueVals=set(featList) #将特征列表创建成为set集合，元素不可重复。创建唯一的分类标签列表
        newEnt=0.0
        IV=0.0
        for value in uniqueVals:     #计算每种划分方式的信息熵
            subdataset=splitdataset(dataset,i,value)
            p=len(subdataset)/float(len(dataset))
            newEnt+=p*jisuanEnt(subdataset)
            IV=IV-p*log(p,2)
        infoGain=baseEnt-newEnt
        if (IV == 0): # fix the overflow bug
            continue
        infoGain_ratio = infoGain / IV                   #这个feature的infoGain_ratio    
        print(u"C4.5中第%d个特征的信息增益率为：%.3f"%(i,infoGain_ratio))
        if (infoGain_ratio >bestInfoGain_ratio):          #选择最大的gain ratio
            bestInfoGain_ratio = infoGain_ratio
            bestFeature = i                              #选择最大的gain ratio对应的feature
    return bestFeature

#CART算法
def CART_chooseBestFeatureToSplit(dataset):

    numFeatures = len(dataset[0]) - 1
    bestGini = 999999.0
    bestFeature = -1
    for i in range(numFeatures):
        featList = [example[i] for example in dataset]
        uniqueVals = set(featList)
        gini = 0.0
        for value in uniqueVals:
            subdataset=splitdataset(dataset,i,value)
            p=len(subdataset)/float(len(dataset))
            subp = len(splitdataset(subdataset, -1, '0')) / float(len(subdataset))
        gini += p * (1.0 - pow(subp, 2) - pow(1 - subp, 2))
        print(u"CART中第%d个特征的基尼值为：%.3f"%(i,gini))
        if (gini < bestGini):
            bestGini = gini
            bestFeature = i
    return bestFeature

def majorityCnt(classList):
    '''
    数据集已经处理了所有属性，但是类标签依然不是唯一的，
    此时我们需要决定如何定义该叶子节点，在这种情况下，我们通常会采用多数表决的方法决定该叶子节点的分类
    '''
    classCont={}
    for vote in classList:
        if vote not in classCont.keys():
            classCont[vote]=0
        classCont[vote]+=1
    sortedClassCont=sorted(classCont.items(),key=operator.itemgetter(1),reverse=True)
    return sortedClassCont[0][0]

#利用ID3算法创建决策树
def ID3_createTree(dataset,labels):
    classList=[example[-1] for example in dataset]
    if classList.count(classList[0]) == len(classList):
        # 类别完全相同，停止划分
        return classList[0]
    if len(dataset[0]) == 1:
        # 遍历完所有特征时返回出现次数最多的
        return majorityCnt(classList)
    bestFeat = ID3_chooseBestFeatureToSplit(dataset)
    bestFeatLabel = labels[bestFeat]
    print(u"此时最优索引为："+(bestFeatLabel))
    ID3Tree = {bestFeatLabel:{}}
    del(labels[bestFeat])
    # 得到列表包括节点所有的属性值
    featValues = [example[bestFeat] for example in dataset]
    uniqueVals = set(featValues)
    for value in uniqueVals:
        subLabels = labels[:]
        ID3Tree[bestFeatLabel][value] = ID3_createTree(splitdataset(dataset, bestFeat, value), subLabels)
    return ID3Tree 

def C45_createTree(dataset,labels):
    classList=[example[-1] for example in dataset]
    if classList.count(classList[0]) == len(classList):
        # 类别完全相同，停止划分
        return classList[0]
    if len(dataset[0]) == 1:
        # 遍历完所有特征时返回出现次数最多的
        return majorityCnt(classList)
    bestFeat = C45_chooseBestFeatureToSplit(dataset)
    bestFeatLabel = labels[bestFeat]
    print(u"此时最优索引为："+(bestFeatLabel))
    C45Tree = {bestFeatLabel:{}}
    del(labels[bestFeat])
    # 得到列表包括节点所有的属性值
    featValues = [example[bestFeat] for example in dataset]
    uniqueVals = set(featValues)
    for value in uniqueVals:
        subLabels = labels[:]
        C45Tree[bestFeatLabel][value] = C45_createTree(splitdataset(dataset, bestFeat, value), subLabels)
    return C45Tree 

def CART_createTree(dataset,labels):
    classList=[example[-1] for example in dataset]
    if classList.count(classList[0]) == len(classList):
        # 类别完全相同，停止划分
        return classList[0]
    if len(dataset[0]) == 1:
        # 遍历完所有特征时返回出现次数最多的
        return majorityCnt(classList)
    bestFeat = CART_chooseBestFeatureToSplit(dataset)
    #print(u"此时最优索引为："+str(bestFeat))
    bestFeatLabel = labels[bestFeat]
    print(u"此时最优索引为："+(bestFeatLabel))
    CARTTree = {bestFeatLabel:{}}
    del(labels[bestFeat])
    # 得到列表包括节点所有的属性值
    featValues = [example[bestFeat] for example in dataset]
    uniqueVals = set(featValues)
    for value in uniqueVals:
        subLabels = labels[:]
        CARTTree[bestFeatLabel][value] = CART_createTree(splitdataset(dataset, bestFeat, value), subLabels)
    return CARTTree 

def classify(inputTree, featLabels, testVec):
    """
    输入：决策树，分类标签，测试数据
    输出：决策结果
    描述：跑决策树
    """
    firstStr = list(inputTree.keys())[0]
    secondDict = inputTree[firstStr]
    featIndex = featLabels.index(firstStr)
    classLabel = '0'
    for key in secondDict.keys():
        if testVec[featIndex] == key:
            if type(secondDict[key]).__name__ == 'dict':
                classLabel = classify(secondDict[key], featLabels, testVec)
            else:
                classLabel = secondDict[key]
    return classLabel

def classifytest(inputTree, featLabels, testDataSet):
    """
    输入：决策树，分类标签，测试数据集
    输出：决策结果
    描述：跑决策树
    """
    classLabelAll = []
    for testVec in testDataSet:
        classLabelAll.append(classify(inputTree, featLabels, testVec))
    return classLabelAll

if __name__ == '__main__':
    filename='dataset.txt'
    testfile='testset.txt'
    dataset, labels = read_dataset(filename)
    #dataset,features=createDataSet()
    print ('dataset',dataset)
    print("---------------------------------------------")
    print(u"数据集长度",len(dataset))
    print ("Ent(D):",jisuanEnt(dataset))
    print("---------------------------------------------")

    print(u"以下为首次寻找最优索引:\n")
    print(u"ID3算法的最优特征索引为:"+str(ID3_chooseBestFeatureToSplit(dataset)))
    print ("--------------------------------------------------")
    print(u"C4.5算法的最优特征索引为:"+str(C45_chooseBestFeatureToSplit(dataset)))
    print ("--------------------------------------------------")
    print(u"CART算法的最优特征索引为:"+str(CART_chooseBestFeatureToSplit(dataset)))
    print(u"首次寻找最优索引结束！")
    print("---------------------------------------------")

    print(u"下面开始创建相应的决策树-------")
    
    while(True):    
        dec_tree=str(input("请选择决策树:->(1:ID3; 2:C4.5; 3:CART)|('enter q to quit!')|："))
        #ID3决策树
        if dec_tree=='1':
            labels_tmp = labels[:] # 拷贝，createTree会改变labels
            ID3desicionTree = ID3_createTree(dataset,labels_tmp)
            print('ID3desicionTree:\n', ID3desicionTree)
            #treePlotter.createPlot(ID3desicionTree)
            treePlotter.ID3_Tree(ID3desicionTree)
            testSet = read_testset(testfile)
            print("下面为测试数据集结果：")
            print('ID3_TestSet_classifyResult:\n', classifytest(ID3desicionTree, labels, testSet))
            print("---------------------------------------------")
        
        #C4.5决策树
        if dec_tree=='2':
            labels_tmp = labels[:] # 拷贝，createTree会改变labels
            C45desicionTree =C45_createTree(dataset,labels_tmp)
            print('C45desicionTree:\n', C45desicionTree)
            treePlotter.C45_Tree(C45desicionTree)
            testSet = read_testset(testfile)
            print("下面为测试数据集结果：")
            print('C4.5_TestSet_classifyResult:\n', classifytest(C45desicionTree, labels, testSet))
            print("---------------------------------------------")
        
        #CART决策树
        if dec_tree=='3':
            labels_tmp = labels[:] # 拷贝，createTree会改变labels        
            CARTdesicionTree = CART_createTree(dataset,labels_tmp)
            print('CARTdesicionTree:\n', CARTdesicionTree)
            treePlotter.CART_Tree(CARTdesicionTree)
            testSet = read_testset(testfile)
            print("下面为测试数据集结果：")
            print('CART_TestSet_classifyResult:\n', classifytest(CARTdesicionTree, labels, testSet))
        if dec_tree=='q':
            break