决策树算法应用

数据集

训练集

RID,age,income,student,credit_rating,class_buys_computer 1,youth,high,no,fair,no 2,youth,high,no,excellent,no 3,middle_aged,high,no,fair,yes 4,senior,medium,no,fair,yes 5,senior,low,yes,fair,yes 6,senior,low,yes,excellent,no 7,middle_aged,low,yes,excellent,yes 8,youth,medium,no,fair,no 9,youth,low,yes,fair,yes 10,senior,medium,yes,fair,yes 11,youth,medium,yes,excellent,yes 12,middle_aged,medium,no,excellent,yes 13,middle_aged,high,yes,fair,yes 14,senior,medium,no,excellent,no

测试集

RID,age,income,student,credit_rating,class_buys_computer 1,youth,high,no,fair,no 2,youth,high,no,excellent,no 3,middle_aged,high,no,fair,yes 4,senior,medium,no,fair,yes 5,senior,low,yes,fair,yes 6,senior,low,yes,excellent,no 7,middle_aged,low,yes,excellent,yes 8,youth,medium,no,fair,no 9,youth,low,yes,fair,yes 10,senior,medium,yes,fair,yes 11,youth,medium,yes,excellent,yes 12,middle_aged,medium,no,excellent,yes 13,youth,medium,no,excellent,yes

　代码

#coding=utf-8 #设置python编码 from sklearn.feature_extraction import DictVectorizer import csv import os from sklearn import preprocessing from sklearn import tree from 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List featureList = [] #类别List , Yes/No labelList = [] 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LabelBinarizer lb = 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 denied testX : [[ 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]