python实现决策树分类（三）

在上一篇文章中，我们已经构建了决策树，接下来可以使用它用于实际的数据分类。在执行数据分类时，需要决策时以及标签向量。程序比较测试数据和决策树上的数值，递归执行直到进入叶子节点。

这篇文章主要使用决策树分类器就行分类，数据集采用UCI数据库中的红酒，白酒数据，主要特征包括12个，主要有非挥发性酸,挥发性酸度,柠檬酸, 残糖含量,氯化物,游离二氧化硫, 总二氧化硫,密度, pH,硫酸盐,酒精,质量等特征。

下面是具体代码的实现：

#coding :utf-8'''2017.6.26 author :Erin           function: "decesion tree" ID3          '''import numpy as npimport pandas as pdfrom math import logimport operator  import randomdef load_data():       red = [line.strip().split(';') for line in open('e:/a/winequality-red.csv')]    white = [line.strip().split(';') for line in open('e:/a/winequality-white.csv')]    data=red+white    random.shuffle(data)  #打乱data    x_train=data[:800]    x_test=data[800:]        features=['fixed','volatile','citric','residual','chlorides','free','total','density','pH','sulphates','alcohol','quality']    return x_train,x_test,featuresdef cal_entropy(dataSet):         numEntries = len(dataSet)    labelCounts = {}    for featVec in dataSet:        label = featVec[-1]        if label not in labelCounts.keys():            labelCounts[label] = 0        labelCounts[label] += 1    entropy = 0.0    for key in labelCounts.keys():        p_i = float(labelCounts[key]/numEntries)        entropy -= p_i * log(p_i,2)#log(x,10)表示以10 为底的对数    return entropydef split_data(data,feature_index,value):    '''    划分数据集    feature_index：用于划分特征的列数，例如“年龄”    value:划分后的属性值：例如“青少年”    '''    data_split=[]#划分后的数据集    for feature in data:        if feature[feature_index]==value:            reFeature=feature[:feature_index]            reFeature.extend(feature[feature_index+1:])            data_split.append(reFeature)    return data_splitdef choose_best_to_split(data):        '''    根据每个特征的信息增益，选择最大的划分数据集的索引特征    '''        count_feature=len(data[0])-1#特征个数4    #print(count_feature)#4    entropy=cal_entropy(data)#原数据总的信息熵    #print(entropy)#0.9402859586706309        max_info_gain=0.0#信息增益最大    split_fea_index = -1#信息增益最大，对应的索引号    for i in range(count_feature):                feature_list=[fe_index[i] for fe_index in data]#获取该列所有特征值        #######################################       # print(feature_list)        unqval=set(feature_list)#去除重复        Pro_entropy=0.0#特征的熵        for value in unqval:#遍历改特征下的所有属性            sub_data=split_data(data,i,value)            pro=len(sub_data)/float(len(data))            Pro_entropy+=pro*cal_entropy(sub_data)            #print(Pro_entropy)                    info_gain=entropy-Pro_entropy        if(info_gain>max_info_gain):            max_info_gain=info_gain            split_fea_index=i    return split_fea_index                ##################################################def most_occur_label(labels):    #sorted_label_count[0][0]  次数最多的类标签    label_count={}    for label in labels:        if label not in label_count.keys():            label_count[label]=0        else:            label_count[label]+=1        sorted_label_count = sorted(label_count.items(),key = operator.itemgetter(1),reverse = True)    return sorted_label_count[0][0]def build_decesion_tree(dataSet,featnames):    '''    字典的键存放节点信息，分支及叶子节点存放值    '''    featname = featnames[:]              ################    classlist = [featvec[-1] for featvec in dataSet]  #此节点的分类情况    if classlist.count(classlist[0]) == len(classlist):  #全部属于一类        return classlist[0]    if len(dataSet[0]) == 1:         #分完了,没有属性了        return Vote(classlist)       #少数服从多数    # 选择一个最优特征进行划分    bestFeat = choose_best_to_split(dataSet)    bestFeatname = featname[bestFeat]    del(featname[bestFeat])     #防止下标不准    DecisionTree = {bestFeatname:{}}    # 创建分支,先找出所有属性值,即分支数    allvalue = [vec[bestFeat] for vec in dataSet]    specvalue = sorted(list(set(allvalue)))  #使有一定顺序    for v in specvalue:        copyfeatname = featname[:]        DecisionTree[bestFeatname][v] =  build_decesion_tree(split_data(dataSet,bestFeat,v),copyfeatname)    return DecisionTreedef classify(Tree, featnames, X):    classLabel=''    root = list(Tree.keys())[0]    firstDict = Tree[root]    featindex = featnames.index(root)  #根节点的属性下标    #classLabel='0'    for key in firstDict.keys():   #根属性的取值,取哪个就走往哪颗子树        if X[featindex] == key:            if type(firstDict[key]) == type({}):                classLabel = classify(firstDict[key],featnames,X)            else:                classLabel = firstDict[key]    return classLabel                if __name__ == '__main__':    x_train,x_test,features=load_data()    split_fea_index=choose_best_to_split(x_train)    newtree=build_decesion_tree(x_train,features)    #print(newtree)    #classLabel=classify(newtree, features, ['7.4','0.66','0','1.8','0.075','13','40','0.9978','3.51','0.56','9.4','5'] )    #print(classLabel)        count=0    for test in x_test:        label=classify(newtree, features,test)                if(label==test[-1]):            count=count+1    acucy=float(count/len(x_test))    print(acucy)    

测试的准确率大概在0.7左右。至此决策树分类算法结束。本文代码地址：https://github.com/lplping/decesion_tree