#-*- coding:utf-8 -*-

from sklearn.feature_extraction importDictVectorizerimportcsvfrom sklearn importpreprocessingfrom sklearn import tree #要求是数值型的值

from sklearn.externals.six importStringIO"""注意： 决策树要求要数值型的值，不能是字符串类型的值

例如： no， yes这样的值是不允许的

需要转换成矩阵

====================================

age income student

youth high no

youth high no

middle_aged high no

senior medium no

senior low yes

====================================

比如上面这种数据：

youth middle_aged senior high medium low ......

1 0 0 1 0 0

1 0 0 1 0 0

....."""allElectronicsData= open(r"C:\Users\Administrator\Desktop\data.xlsx", ‘r‘)

reader=csv.reader(allElectronicsData)print(reader)

headers=next(reader)print(headers)#["RID", ‘age‘.....]

featureList=[]

labelList=[]for row inreader:

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)#[#{"credit_rating": "fair", "age": "youth"},#.... #作用，方便转换成矩阵。将数据转换成对象#]

vec=DictVectorizer()

dummyX=vec.fit_transform(featureList).toarray()print("dummyX:" + str(dummyX)) #转换成矩阵的数据了二维

print(vec.get_feature_names())print("labelList:" +str(labelList))

lb=preprocessing.LabelBinarizer()

dummyY=lb.fit_transform(labelList)print("dummyY:" +str(dummyY))

clf= tree.DecisionTreeClassifier(criterion="entropy")

clf=clf.fit(dummyX, dummyY)print("clf:", str(clf))#存储决策树信息

#Graphviz 将dot转换成pdf的命令： dot -T pdf iris.dot -o output.pdf#可以查看decision tree 的形状了（看pdf的值）

with open(r"C:\Users\Administrator\Desktop\code\mechine_learning\allElectronicInformationGainOri.dot", "w") as f:

f= tree.export_graphviz(clf, feature_names = vec.get_feature_names(), out_file =f)#下面的代码属于预测的代码#属于转化后的矩阵数值，其实就是进行复制修改

oneRowX =dummyX[0, :]print("oneRowX:" +str(oneRowX))

newRowX=oneRowX

newRowX[0]= 1newRowX[2] =0print("newRowX:", str(newRowX))

predictedY=clf.predicted(newRowX)#预测 class_buys_labels的值

predicted("predictedY:" +str(predictedY))if __name__ == ‘__main__‘:

main()