ID3算法实现

最近在学树模型，所以今天花了点时间把机器学习实战上的ID3算法敲了一遍。这个算法比较简单，不过我也是看着书敲的，因为python还不够熟悉，所以一边学点python的函数什么的。代码这边留个档，以后好回头看看。

# -*- coding: utf-8 -*-
"""
@author: 沈同学
"""
from math import log
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 splitDateSet(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=splitDateSet(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
#--------------------------------------------------------------------------------
import operator
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:
        subLabels=labels[:]
        myTree[bestFeatLabel][value]=createTree(splitDateSet(dataSet,bestFeat,value),subLabels)
    return myTree
#----------------------------------------------------------------------------------