本文实例为大家分享了python编写决策树源代码，供大家参考，具体内容如下

因为最近实习的需要，所以用python里的sklearn包重新写了一次决策树。

工具：sklearn，将dot文件转化为pdf格式（是为了将形成的决策树可视化）graphviz-2.38，下载解压之后将其中的bin文件的目录添加进环境变量

源代码如下：

from sklearn.feature_extraction import DictVectorizer

import csv

from sklearn import tree

from sklearn import preprocessing

from sklearn.externals.six import StringIO

from xml.sax.handler import feature_external_ges

from numpy.distutils.fcompiler import dummy_fortran_file

# Read in the csv file and put features into list of dict and list of class label

allElectronicsData = open(r'E:/DeepLearning/resources/AllElectronics.csv', 'rt')

reader = csv.reader(allElectronicsData)

headers = next(reader)

featureList = []

lableList = []

for row in reader:

lableList.append(row[len(row)-1])

rowDict = {}

#不包括len(row)-1

for i in range(1,len(row)-1):

rowDict[headers[i]] = row[i]

featureList.append(rowDict)

print(featureList)

vec = DictVectorizer()

dummX = vec.fit_transform(featureList).toarray()

print(str(dummX))

lb = preprocessing.LabelBinarizer()

dummY = lb.fit_transform(lableList)

print(str(dummY))

#entropy=>ID3

clf = tree.DecisionTreeClassifier(criterion='entropy')

clf = clf.fit(dummX, dummY)

print("clf:"+str(clf))

#可视化tree

with open("resultTree.dot",'w')as f:

f = tree.export_graphviz(clf, feature_names=vec.get_feature_names(),out_file = f)

#对于新的数据怎样来查看它的分类

oneRowX = dummX[0,:]

print("oneRowX: "+str(oneRowX))

newRowX = oneRowX

newRowX[0] = 1

newRowX[2] = 0

predictedY = clf.predict(newRowX)

print("predictedY: "+ str(predictedY))

这里的AllElectronics.csv，形式如下图所示：

今天早上好不容易将jdk、eclipse以及pydev装进linux，但是，但是，但是，想装numpy的时候，总是报错，发现是没有gcc，然后又去装gcc，真是醉了，到现在gcc还是没有装成功，再想想方法