在上一篇文章中，我們已經構建了決策樹，接下來可以使用它用于實際的數據分類。在執行數據分類時，需要決策時以及標簽向量。程序比較測試數據和決策樹上的數值，遞歸執行直到進入葉子節點。

這篇文章主要使用決策樹分類器就行分類，數據集采用UCI數據庫中的紅酒，白酒數據，主要特征包括12個，主要有非揮發性酸,揮發性酸度, 檸檬酸, 殘糖含量,氯化物, 游離二氧化硫, 總二氧化硫,密度, pH,硫酸鹽,酒精, 質量等特征。

下面是具體代碼的實現：

#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) #打亂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 為底的對數  return entropy def split_data(data,feature_index,value):  '''  劃分數據集  feature_index：用于劃分特征的列數，例如“年齡”  value:劃分后的屬性值：例如“青少年”  '''  data_split=[]#劃分后的數據集  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):    '''  根據每個特征的信息增益，選擇最大的劃分數據集的索引特征  '''    count_feature=len(data[0])-1#特征個數4  #print(count_feature)#4  entropy=cal_entropy(data)#原數據總的信息熵  #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，對應的索引號   for i in range(count_feature):        feature_list=[fe_index[i] for fe_index in data]#獲取該列所有特征值    #######################################     # print(feature_list)    unqval=set(feature_list)#去除重復    Pro_entropy=0.0#特征的熵    for value in unqval:#遍歷改特征下的所有屬性      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] 次數最多的類標簽  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):  '''  字典的鍵存放節點信息，分支及葉子節點存放值  '''  featname = featnames[:]       ################  classlist = [featvec[-1] for featvec in dataSet] #此節點的分類情況  if classlist.count(classlist[0]) == len(classlist): #全部屬于一類    return classlist[0]  if len(dataSet[0]) == 1:     #分完了,沒有屬性了    return Vote(classlist)    #少數服從多數  # 選擇一個最優特征進行劃分  bestFeat = choose_best_to_split(dataSet)  bestFeatname = featname[bestFeat]  del(featname[bestFeat])   #防止下標不準  DecisionTree = {bestFeatname:{}}  # 創建分支,先找出所有屬性值,即分支數  allvalue = [vec[bestFeat] for vec in dataSet]  specvalue = sorted(list(set(allvalue))) #使有一定順序  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) #根節點的屬性下標  #classLabel='0'  for key in firstDict.keys():  #根屬性的取值,取哪個就走往哪顆子樹    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)