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决策树算法简单应用

时间:2018-04-09 00:25:07      阅读:226      评论:0      收藏:0      [点我收藏+]

标签:append   exce   算法   信息   dict   atom   变量   函数   file   

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:

技术分享图片

 

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(rD:\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:

技术分享图片

 

画好后是pdf形式的,看一下:

技术分享图片

 

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

 

决策树算法简单应用

标签:append   exce   算法   信息   dict   atom   变量   函数   file   

原文地址:https://www.cnblogs.com/xuyiqing/p/8748029.html

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