熵：H(D)=-Plog2(P)

info(A)=Info(D)-Info_A(D)

打开CSV文件:

分析：属性 ：age income student credit_rating 类：buys_computer

共14人

 

 

from sklearn.feature_extraction import DictVectorizer

import csv

from sklearn import tree

from sklearn import preprocessing  #数据的预处理

from sklearn.externals.six import StringIO

# 打开CSV文件

allElectionicsData=open(r'G:\MachineLearning/AllElectronics.csv','rt')

reader=csv.reader(allElectionicsData)

headers=next(reader)

print("结果是：")

print(headers)

#分阶段展示结果：

 

 

featureList=[]  #featureList是属性列表

labelList=[]  # labelList是类列表

for row in reader:  # 对每行进行循环遍历

labelList.append(row[len(row)-1]) 

rowDict={}    #字典

for i in range(1,len(row)-1):

rowDict[headers[i]]=row[i]  

featureList.append(rowDict)   #类

print(featureList)

结果：[{'age': 'youth', 'income': 'high', 'student': 'no', 'credit_rating': 'fair'},

{'age': 'youth', 'income': 'high', 'student': 'no', 'credit_rating': 'excellent'},

{'age': 'middle_aged', 'income': 'high', 'student': 'no', 'credit_rating': 'fair'},

{'age': 'senior', 'income': 'medium', 'student': 'no', 'credit_rating': 'fair'},

{'age': 'senior', 'income': 'low', 'student': 'yes', 'credit_rating': 'fair'},

{'age': 'senior', 'income': 'low', 'student': 'yes', 'credit_rating': 'excellent'},

{'age': 'middle_aged', 'income': 'low', 'student': 'yes', 'credit_rating': 'excellent'},

{'age': 'youth', 'income': 'medium', 'student': 'no', 'credit_rating': 'fair'},

{'age': 'youth', 'income': 'low', 'student': 'yes', 'credit_rating': 'fair'},

{'age': 'senior', 'income': 'medium', 'student': 'yes', 'credit_rating': 'fair'},

{'age': 'youth', 'income': 'medium', 'student': 'yes', 'credit_rating': 'excellent'},

{'age': 'middle_aged', 'income': 'medium', 'student': 'no', 'credit_rating': 'excellent'},

{'age': 'middle_aged', 'income': 'high', 'student': 'yes', 'credit_rating': 'fair'},

{'age': 'senior', 'income': 'medium', 'student': 'no', 'credit_rating': 'excellent'}]

 将14个数据初步转换成14行列表的形式：便于下一步利用vec.fit_transform(feature).toarray

#Vetorize features vec=DictVectorizer() dummyX=vec.fit_transform(featureList).toarray()　　#转换0，1，scikit库可以识别 print("dummyX:"+str(dummyX))

 

print(vec.get_feature_names()) ['age=middle_aged', 'age=senior', 'age=youth', 'credit_rating=excellent', 'credit_rating=fair', 'income=high', 'income=low', 'income=medium', 'student=no', 'student=yes'] # labelList存放类

print("labelList:"+str(labelList)) lb=preprocessing.LabelBinarizer() dummY=lb.fit_transform(labelList) print("dummyY:"+str(dummY) #选择器 clf=tree.DecisionTreeClassifier() clf=tree.DecisionTreeClassifier(criterion='entropy') #熵 entropy clf=clf.fit(dummyX,dummY) print("clf:"+str(clf))

 

 

 

 # Visualize model

with open("allElectronicInformationGainOri.dot", 'w') as f:

f = tree.export_graphviz(clf, feature_names=vec.get_feature_names(), out_file=f)

oneRowX = dummyX[0, :].reshape(1,-1)

print("oneRowX: " + str(oneRowX))

newRowX = oneRowX

newRowX[0][0] = 1

newRowX[0][2] = 0

print("newRowX: " + str(newRowX))

predictedY = clf.predict(newRowX)

print("predictedY: " + str(predictedY))

 

　　

 

  

 注意维度的变换