python实现决策树分类（2）

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,features def 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 entropy def 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 DecisionTree def 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

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