3.2 决策树算法应用

1. Python

2. Python机器学习的库：scikit-learn

   2.1： 特性：
   简单高效的数据挖掘和机器学习分析
   对所有用户开放，根据不同需求高度可重用性
   基于Numpy, SciPy和matplotlib
   开源，商用级别：获得 BSD许可

   2.2 覆盖问题领域：
   分类（classification), 回归（regression), 聚类（clustering), 降维(dimensionality reduction)
   模型选择(model selection), 预处理(preprocessing)

3. 使用用scikit-learn
   安装scikit-learn: pip, easy_install, windows installer
   安装必要package：numpy， SciPy和matplotlib， 可使用Anaconda (包含numpy, scipy等科学计算常用
   package）
   安装注意问题：Python解释器版本（2.7 or 3.4？）, 32-bit or 64-bit系统

4. 例子：
   

from sklearn.feature_extraction import DictVectorizer
import csv
from sklearn import  tree, preprocessing
from sklearn.externals.six import StringIO
import numpy  as np


allElectronicsData=open(r'C://AllElectronics.csv')
reader=csv.reader(allElectronicsData)
headers=reader.next()
print(headers)

featrueList=[]
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]
    featrueList.append(rowDict)
print(featrueList)

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

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


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

print("dummyY:"+str(dummyY))

clf = tree.DecisionTreeClassifier(criterion="entropy")
clf=clf.fit(dummyX,dummyY)
print("clf:"+str(clf))

with open("allElectronicInformationGainOri.dot",'w') as f:
    f=tree.export_graphviz(clf,out_file=f,feature_names=vec.get_feature_names())

oneRowX=dummyX[0,:]
print("oneRowx:"+str(oneRowX))        

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

predictedY = clf.predict(newRowX)
print("predictedY:"+str(predictedY))