决策树练习
'''
@author :Eric-chen
@contact:809512722@qq.com
@time :2017/12/19 16:19
@desc :
'''
from sklearn.feature_extraction import DictVectorizer
import csv
from sklearn import preprocessing
from sklearn import tree
from sklearn.externals.six import StringIO#read in the csv file and put features in a list of dict and list of class label
allData=open(r'F:\python\day01\AllElectronics.csv','rb')
reader=csv.reader(allData)
headers=reader.next()
print (headers)
featureList=[]
labelList=[]
for row in reader:labelList.append(row[len(row)-1])rowDict={}for i in range(1,len(row)-1):# print (row[i])# print ("==")rowDict[headers[i]]=row[i]# print (rowDict)featureList.append(rowDict)
print (featureList)# Vetorize features
vec=DictVectorizer()
dummyX=vec.fit_transform(featureList).toarray()
print ("dummyX:"+str(dummyX))
print (vec.get_feature_names())
print ("labellist:"+str(labelList))
# Vectorize class labels
lb=preprocessing.LabelBinarizer()
dummY=lb.fit_transform(labelList)
print("dummY"+str(dummY))# Using decision tree for classification
clf=tree.DecisionTreeClassifier(criterion='entropy')
clf=clf.fit(dummyX,dummY)
print("clf:"+str(clf))# Visualize model
with open("allData.dot",'w') as f:f=tree.export_graphviz(clf,feature_names=vec.get_feature_names(),out_file=f)
oneRowX=dummyX[0,:]
print ("oneRowX:"+str(oneRowX))
newRowX=oneRowX
newRowX[0]=1
newRowX[2]=0print("newRowx:"+str(newRowX))
predictedY=clf.predict(newRowX.reshape(1, -1))
print("predictedY"+str(predictedY))
posted @ 2017-12-19 18:20 酸奶加绿茶 阅读( ...) 评论( ...) 编辑 收藏