1-2 决策树算法应用

决策树算法应用

数据集

训练集

RID,age,income,student,credit_rating,class_buys_computer1,youth,high,no,fair,no2,youth,high,no,excellent,no3,middle_aged,high,no,fair,yes4,senior,medium,no,fair,yes5,senior,low,yes,fair,yes6,senior,low,yes,excellent,no7,middle_aged,low,yes,excellent,yes8,youth,medium,no,fair,no9,youth,low,yes,fair,yes10,senior,medium,yes,fair,yes11,youth,medium,yes,excellent,yes12,middle_aged,medium,no,excellent,yes13,middle_aged,high,yes,fair,yes14,senior,medium,no,excellent,no

测试集

RID,age,income,student,credit_rating,class_buys_computer1,youth,high,no,fair,no2,youth,high,no,excellent,no3,middle_aged,high,no,fair,yes4,senior,medium,no,fair,yes5,senior,low,yes,fair,yes6,senior,low,yes,excellent,no7,middle_aged,low,yes,excellent,yes8,youth,medium,no,fair,no9,youth,low,yes,fair,yes10,senior,medium,yes,fair,yes11,youth,medium,yes,excellent,yes12,middle_aged,medium,no,excellent,yes13,youth,medium,no,excellent,yes

　代码

#coding=utf-8#设置python编码from sklearn.feature_extraction import DictVectorizerimport csvimport osfrom sklearn import preprocessingfrom sklearn import treefrom sklearn.externals.six import StringIO#---数据获取---#使用CSV包，按行读取CSV数据allElectronicsData = open(r'./AllElectronics.csv','rb')reader = csv.reader(allElectronicsData)#获取各个字段及其名headers = reader.next()print("headers : " ,headers)#---数据预处理---#sklearn只接受数值型的数据#以CSV中第一行age数据为例#age：youth middle_age senior#矩阵:  1       0        0#特征值ListfeatureList = []#类别List , Yes/NolabelList = []for row in reader:    #将每一行的结果放入labelList    labelList.append(row[len(row)-1])    #对每一行数据创建一个字典(将每行特征数据转为JSON格式),将headers中的字段与实际值对应 如age:youth    rowDict = {}    #i从1开始，取消RID的影响    for i in range(1,len(row)-1):        rowDict[headers[i]] = row[i]    featureList.append(rowDict)# print labelList# print featureList#把featureList向量化vec = DictVectorizer()dummyX = vec.fit_transform(featureList).toarray()print("dummyX : " + str(dummyX))print ("feature mapping : " + str(vec.get_feature_names()))#把labelList向量化,使用python自带LabelBinarizerlb = preprocessing.LabelBinarizer()dummyY = lb.fit_transform(labelList)# print("dummyY : " + str(dummyY))#使用tree分类器创建,使用信息熵 ID3算法clf = tree.DecisionTreeClassifier(criterion='entropy')clf = clf.fit(dummyX,dummyY)print ("clf: " +  str(clf))#创建dot文件并输出树数据with open('DTreeData.dot','w') as f:    f = tree.export_graphviz(clf,feature_names= vec.get_feature_names(),out_file=f)os.system("dot -Tpdf D:\Data\MyCode\codepython\ML_Base_Demo\DecisionTree\DTreeData.dot -o D:\Data\MyCode\codepython\ML_Base_Demo\DecisionTree\DTree.pdf")#利用生成的决策树进行预测# oneRow = dummyX[1,:]# print ("one row : " + str(oneRow))## newRow = oneRow# newRow[0] = 1# newRow[2] = 0# print("new row x : " + str(newRow))## predictedY = clf.predict(newRow)## print ("predict result : " + str(predictedY))testSet = open(r'test_set.csv','rb')reader = csv.reader(testSet)reader.next()testList = []for row in reader:    #将每一行的结果放入labelList    labelList.append(row[len(row)-1])    #对每一行数据创建一个字典(将每行特征数据转为JSON格式),将headers中的字段与实际值对应 如age:youth    rowDict = {}    #i从1开始，取消RID的影响    for i in range(1,len(row)-1):        rowDict[headers[i]] = row[i]    testList.append(rowDict)# print testList#把testList向量化vec = DictVectorizer()testX = vec.fit_transform(testList).toarray()print("testX : " + str(testX))predictSet = clf.predict(testX)print predictSet

运行结果

('headers : ', ['RID', 'age', 'income', 'student', 'credit_rating', 'class_buys_computer'])dummyX : [[ 0.  0.  1.  0.  1.  1.  0.  0.  1.  0.] [ 0.  0.  1.  1.  0.  1.  0.  0.  1.  0.] [ 1.  0.  0.  0.  1.  1.  0.  0.  1.  0.] [ 0.  1.  0.  0.  1.  0.  0.  1.  1.  0.] [ 0.  1.  0.  0.  1.  0.  1.  0.  0.  1.] [ 0.  1.  0.  1.  0.  0.  1.  0.  0.  1.] [ 1.  0.  0.  1.  0.  0.  1.  0.  0.  1.] [ 0.  0.  1.  0.  1.  0.  0.  1.  1.  0.] [ 0.  0.  1.  0.  1.  0.  1.  0.  0.  1.] [ 0.  1.  0.  0.  1.  0.  0.  1.  0.  1.] [ 0.  0.  1.  1.  0.  0.  0.  1.  0.  1.] [ 1.  0.  0.  1.  0.  0.  0.  1.  1.  0.] [ 1.  0.  0.  0.  1.  1.  0.  0.  0.  1.] [ 0.  1.  0.  1.  0.  0.  0.  1.  1.  0.]]feature mapping : ['age=middle_aged', 'age=senior', 'age=youth', 'credit_rating=excellent', 'credit_rating=fair', 'income=high', 'income=low', 'income=medium', 'student=no', 'student=yes']clf: DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,            max_features=None, max_leaf_nodes=None,            min_impurity_split=1e-07, min_samples_leaf=1,            min_samples_split=2, min_weight_fraction_leaf=0.0,            presort=False, random_state=None, splitter='best')Error: Could not open "D:\Data\MyCode\codepython\ML_Base_Demo\DecisionTree\DTree.pdf" for writing : Permission deniedtestX : [[ 0.  0.  1.  0.  1.  1.  0.  0.  1.  0.] [ 0.  0.  1.  1.  0.  1.  0.  0.  1.  0.] [ 1.  0.  0.  0.  1.  1.  0.  0.  1.  0.] [ 0.  1.  0.  0.  1.  0.  0.  1.  1.  0.] [ 0.  1.  0.  0.  1.  0.  1.  0.  0.  1.] [ 0.  1.  0.  1.  0.  0.  1.  0.  0.  1.] [ 1.  0.  0.  1.  0.  0.  1.  0.  0.  1.] [ 0.  0.  1.  0.  1.  0.  0.  1.  1.  0.] [ 0.  0.  1.  0.  1.  0.  1.  0.  0.  1.] [ 0.  1.  0.  0.  1.  0.  0.  1.  0.  1.] [ 0.  0.  1.  1.  0.  0.  0.  1.  0.  1.] [ 1.  0.  0.  1.  0.  0.  0.  1.  1.  0.] [ 0.  0.  1.  1.  0.  0.  0.  1.  1.  0.]][0 0 1 1 1 0 1 0 1 1 1 1 0]