python利用sklearn包编写决策树源代码_Python

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))