机器学习——决策树的实现

#!/usr/bin/env python
#-*-coding:utf-8-*-
#决策树的建立，训练测试，
from sklearn.feature_extraction import DictVectorizer
import csv
from sklearn import preprocessing
from sklearn import tree
from sklearn.externals.six import StringIO
 
#读数据
allElectronicsData=open(r'jueceshu.csv','rb')
reader=csv.reader(allElectronicsData)
headers=reader.next()
 
featureList=[]
labelList=[]
#分析数据
for row in reader:
    #print(row)
    if(row):
        labelList.append(row[len(row)-1])
        rowDict={}
        for i in range(1,len(row)-1):
            rowDict[headers[i]]=row[i]
        featureList.append(rowDict)
 
print(featureList)
#转化数据
vec=DictVectorizer()
dummyX=vec.fit_transform(featureList).toarray()
 
print('dummyX:'+str(dummyX))
print(vec.get_feature_names())
 
print('labelList:'+str(labelList))
 
lb=preprocessing.LabelBinarizer()
dummyY=lb.fit_transform(labelList)
print('dummyY:'+str(dummyX))
 
#训练数据
clf=tree.DecisionTreeClassifier(criterion='entropy')
clf=clf.fit(dummyX,dummyY)
print('clf'+str(clf))
#转化为dot模式
with open('allElectronicInformationGainDri.dot','w') as f:
    f=tree.export_graphviz(clf,feature_names=vec.get_feature_names(),out_file=f)
 
#决策树的预测
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