决策树算法简单应用

Posted 2020-10-30 xuyiqing

采用ID3算法

（信息熵：H(X)=?∑i=0np(xi)log2p(xi)）

 

下载一个决策树可视化软件：Graphviz

（注意环境变量Path加：C:\\Program Files (x86)\\Graphviz2.38\\bin）

 

代码：

导入需要用到的库：

from sklearn.feature_extraction import DictVectorizer
import csv
from sklearn import tree
from sklearn import preprocessing

 

读取表格：

 

这里一些数据（属性），决定一位客户是否要买这台电脑

读取表格并做一些简单的数据处理：

allElectronicsData = open(r‘D:\\demo.csv‘, ‘rt‘)
reader = csv.reader(allElectronicsData)
headers = next(reader)

featureList = []
labelList = []

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)

看一下结果：

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

 

处理的不错：

调用sklearn的函数进一步处理数据：

vec = DictVectorizer()
dummyX = vec.fit_transform(featureList) .toarray()

lb = preprocessing.LabelBinarizer()
dummyY = lb.fit_transform(labelList)

 

查看下处理的结果：

print("dummyX: \\n" + str(dummyX))
print(vec.get_feature_names())

print("labelList: " + str(labelList))
print("dummyY: \\n" + str(dummyY))

结果：

注意要把数据转换成数字矩阵，便于学习

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]]

 

用决策树ID3算法和训练数据拟合分类器模型：

clf = tree.DecisionTreeClassifier(criterion=‘entropy‘)
clf = clf.fit(dummyX, dummyY)

 

可以利用下载的可视化软件画图观察下：

with open(r"D:\\demo.dot", ‘w‘) as f:
    f = tree.export_graphviz(clf, feature_names=vec.get_feature_names(), out_file=f)

然后调出cmd：

 

画好后是pdf形式的，看一下：

 

模型建好了，我们可以做一个预测：

在第一个数据的基础上修改下，然后预测是否买电脑：

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))

结果：

oneRowX: [0. 0. 1. 0. 1. 1. 0. 0. 1. 0.]
newRowX: [1. 0. 0. 0. 1. 1. 0. 0. 1. 0.]
predictedY: [1]

 

结论：这个人要买这台电脑