如何使用Pydev实现简单的决策树算法以及可视化

该代码是根据麦子学院的机器学习教程中的决策树算法课程内容:

1.csv的文件内容

rid,age,income,student,credit_rating,class_buy_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

2.编写代码：

from sklearn.feature_extraction import DictVectorizer
import csv
from sklearn import preprocessing
from sklearn import tree
from sklearn.externals.six import StringIO

#读取csv文件的标题行
allElectronicsData = open(r'D:\buy_computer.csv','rt')
reader = csv.reader(allElectronicsData)
headers = next(reader)
print(headers)

featureList=[]
labelList=[]

#读取csv文件的内容行，并整理成字典格式
for row in reader:
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)
print(labelList)

#使用DictVectorizer中的各个属性列的不同属性值转换成0，1格式
vec = DictVectorizer()
dummyX = vec.fit_transform(featureList).toarray()

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

#使用DictVectorizer中的标签列的不同属性值转换成0，1格式
lb=preprocessing.LabelBinarizer()
dummyY = lb.fit_transform(labelList)
print("dummyY :"+str(dummyY))

#使用tree的中DecisionTreeClassifier的信息熵差值方法构建决策树
clf = tree.DecisionTreeClassifier(criterion="entropy")
clf = clf.fit(dummyX, dummyY)
print("clf:" + str(clf))

#使用tree的中DecisionTreeClassifier的信息熵差值方法构建决策树
with open("allElectronicInformationGainOri.dot",'w') as f:
f=tree.export_graphviz(clf,feature_names=vec.get_feature_names(),out_file=f)

#使用构建好的决策树来预测新的样本标签值
oneRowX = dummyX[0,:]
print("oneRowX:"+str(oneRowX))

newRowX = oneRowX

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

predictedY= clf.predict(newRowX.reshape(1, -1))

print("predictedY"+str(predictedY))

Console输出的结果为：

[‘rid’, ‘age’, ‘income’, ‘student’, ‘credit_rating’, ‘class_buy_computer’]

[{‘age’: ‘youth’, ‘income’: ‘high’, ‘student’: ‘no’, ‘credit_rating’: ‘fair’}, {‘age’: ‘youth’, ‘income’: ‘high’, ‘student’: ‘no’, ‘credit_rating’: ‘excellent’}, {‘age’: ‘middle_aged’, ‘income’: ‘high’, ‘student’: ‘no’, ‘credit_rating’: ‘fair’}, {‘age’: ‘senior’, ‘income’: ‘medium’, ‘student’: ‘no’, ‘credit_rating’: ‘fair’}, {‘age’: ‘senior’, ‘income’: ‘low’, ‘student’: ‘yes’, ‘credit_rating’: ‘fair’}, {‘age’: ‘senior’, ‘income’: ‘low’, ‘student’:
4000
‘yes’, ‘credit_rating’: ‘excellent’}, {‘age’: ‘middle_aged’, ‘income’: ‘low’, ‘student’: ‘yes’, ‘credit_rating’: ‘excellent’}, {‘age’: ‘youth’, ‘income’: ‘medium’, ‘student’: ‘no’, ‘credit_rating’: ‘fair’}, {‘age’: ‘youth’, ‘income’: ‘low’, ‘student’: ‘yes’, ‘credit_rating’: ‘fair’}, {‘age’: ‘senior’, ‘income’: ‘medium’, ‘student’: ‘yes’, ‘credit_rating’: ‘fair’}, {‘age’: ‘youth’, ‘income’: ‘medium’, ‘student’: ‘yes’, ‘credit_rating’: ‘excellent’}, {‘age’: ‘middle_aged’, ‘income’: ‘medium’, ‘student’: ‘no’, ‘credit_rating’: ‘excellent’}, {‘age’: ‘middle_aged’, ‘income’: ‘high’, ‘student’: ‘yes’, ‘credit_rating’: ‘fair’}, {‘age’: ‘senior’, ‘income’: ‘medium’, ‘student’: ‘no’, ‘credit_rating’: ‘excellent’}]

[‘no’, ‘no’, ‘yes’, ‘yes’, ‘yes’, ‘no’, ‘yes’, ‘no’, ‘yes’, ‘yes’, ‘yes’, ‘yes’, ‘yes’, ‘no’]

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.]]

[‘age=middle_aged’, ‘age=senior’, ‘age=youth’, ‘credit_rating=excellent’, ‘credit_rating=fair’, ‘income=high’, ‘income=low’, ‘income=medium’, ‘student=no’, ‘student=yes’]

labelList:[‘no’, ‘no’, ‘yes’, ‘yes’, ‘yes’, ‘no’, ‘yes’, ‘no’, ‘yes’, ‘yes’, ‘yes’, ‘yes’, ‘yes’, ‘no’]

dummyY :[[0]

[0]

[1]

[1]

[1]

[0]

[1]

[0]

[1]

[1]

[1]

[1]

[1]

[0]]

clf:DecisionTreeClassifier(class_weight=None, criterion=’entropy’, max_depth=None,

max_features=None, max_leaf_nodes=None,

min_impurity_decrease=0.0, min_impurity_split=None,

min_samples_leaf=1, min_samples_split=2,

min_weight_fraction_leaf=0.0, presort=False, random_state=None,

splitter=’best’)

oneRowX:[ 0. 0. 1. 0. 1. 1. 0. 0. 1. 0.]

newRowX:[ 1. 0. 0. 0. 1. 1. 0. 0. 1. 0.]

predictedY[1]

3.决策树dot文件的可视化（使用Graphviz软件将dot文件转化为pdf格式）

Graphviz下载地址：http://www.graphviz.org/download/

选择Windows下面的

Stable 2.38 Windows install packages 点击选择zip压缩包即可，然后解压。

在系统环境变量中添加该文件的安装目录下的bin（例如我的目录为：C:\Users\Administrator\Desktop\小论文外文文献-深度稀疏自动编码器\graphviz-2.38\release\bin，首先在原始的path值后面添加分号，然后再加上bin的路径）

可以将项目生成的allElectronicInformationGainOri.dot（可以右键点击“属性”，右面Location的值即为文件路径）

转换成pdf的命令：

打开cmd命令窗口，输入：dot -Tpdf E:\Workpace\AnacondaPyTest\src\allElectronicInformationGainOri.dot-o d:\output.pdf

即在d盘下生成了output.pdf文件，打开文件，内容如下：