from math import *
import operator
def calcShannonEnt(dataSet):
numEntries=len(dataSet)
labelCounts={}
for featVec in dataSet:
currentLabel=featVec[-1]
if currentLabel not in labelCounts.keys():
labelCounts[currentLabel]=0
labelCounts[currentLabel]+=1
shannonEnt=0.0
for key in labelCounts:
prob=float(labelCounts[key])/numEntries
shannonEnt-=prob*log(prob,2)
return shannonEnt
def createDataSet():
dataSet=[[1,1,'yes'],
[1,1,'yes'],
[1,0,'no'],
[0,1,'no'],
[0,1,'no']]
labels = ['no surfacing','flippers']
return dataSet,labels
def splitDataSet(dataSet,axis,value):
retDataSet=[]
for featVec in dataSet:
if featVec[axis]==value:
reducedFeatVec=featVec[:axis]
reducedFeatVec.extend(featVec[axis+1:])
retDataSet.append(reducedFeatVec)
return retDataSet
def chooseBestFeatureToSplit(dataSet):
numFeatures=len(dataSet[0])-1
baseEntropy=calcShannonEnt(dataSet)
bestInfoGain=0.0;bestFeature=-1
for i in range(numFeatures):
featList=[example[i] for example in dataSet]
uniqueVals=set(featList)
newEntropy=0.0
for value in uniqueVals:
subDataSet=splitDataSet(dataSet,i,value)
prob=len(subDataSet)/float(len(dataSet))
newEntropy+=prob*calcShannonEnt(subDataSet)
infoGain=baseEntropy-newEntropy
if(infoGain>bestInfoGain):
bestInfoGain=infoGain
bestFeature=i
return bestFeature
def majorityCnt(classList):
classCount={}
for vote in classList:
if vote not in classCount.keys():classCount[vote]=0
classCount[vote]+=1
sortedClassCount=sorted(classCount.iteritems(),\
key=operator.itemgetter(1),reverse=True)
return sortedClassCount[0][0]
def createTree(dataSet,labels):
classList=[example[-1] for example in dataSet]
if classList.count(classList[0])==len(classList):
return classList[0]
if len(dataSet[0])==1:
return majorityCnt(classList)
bestFeat=chooseBestFeatureToSplit(dataSet)
bestFeatLabel=labels[bestFeat]
myTree={bestFeatLabel:{}}
del(labels[bestFeat])
featValues=[example[bestFeat] for example in dataSet]
uniqueVals=set(featValues)
for value in uniqueVals:
subLables=labels[:]
myTree[bestFeatLabel][value]=createTree(splitDataSet\
(dataSet, bestFeat,value),subLables)
return myTree
def classify(inputTree,featLabels,testVec):
firstStr=inputTree.keys()[0]
secondDict=inputTree[firstStr]
featIndex=featLabels.index(firstStr)
for key in secondDict.keys():
if testVec[featIndex]==key:
if type(secondDict[key]).__name__=='dict':
classLabel=classify(secondDict[key],featLabels,testVec)
else: classLabel=secondDict[key]
return classLabel
def storeTree(inputTree,filename):
import pickle
fw=open(filename,'w')
pickle.dump(inputTree,fw)
fw.close()
def grabTree(filename):
import pickle
fr=open(filename)
return pickle.load(fr)
相较于KNN算法，决策树可以对数据持久化，将分类器存储在硬盘上，不用每次对数据分类时重新学习一遍。
自动保存在python的当前工作目录中
