[置顶] ID3算法的python实现

这篇文章的内容接着http://blog.csdn.net/xueyunf/article/details/9214727的内容，所有还有部分函数在http://blog.csdn.net/xueyunf/article/details/9212827中，由于这个算法需要理解的内容比较多，所以我分成了3篇分别介绍，因为自己也是用了3天的时间才理解了这一经典算法。当然很犀利的童鞋也许很短时间就理解了这一算法，那么这篇文章也就不适合你了，可以跳过了，读了后不会有太多收获的。

下面我就贴出代码来，为初学者提示一点东西：

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(splitDataSet(dataSet, bestFeat, value), subLabels)
    return myTree

第一个函数为选出出现次数最多的分类名称。

第二个函数式建立决策树，也就是今天我想说的最关键的部分的代码，我们可以发现这是一个递归函数，首先我来说明跳出递归的条件，也就是类别完全相同时跳出递归，或者我们将所有的特征已经用尽则跳出递归。我们不难发现，第一个if是第一种情况，第二个if对应第二种情况。

然后我们来处理不是这两种情况的情况，每次都利用前面的选择最优划分将数据进行划分，同时将该标签插入树中，并删除该标签，然后再次将剩下的数据和标签形成的新的结构放入函数中递归进行构建子决策树，这样一棵完整的决策树就建立了。

下面给出程序运行的截图：(所谓有图有真相，无图无真相啊，我用的python的开发IDE是Eric5顺便推荐给大家)

最后给大家3篇文章所有的代码：

import math
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*math.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):
    numberFeatures = len(dataSet[0])-1
    baseEntropy = calcShannonEnt(dataSet)
    bestInfoGain = 0.0;
    bestFeature = -1;
    for i in range(numberFeatures):
        featList = [example[i] for example in dataSet]
        print(featList)
        uniqueVals = set(featList)
        print(uniqueVals)
        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:
        subLabels = labels[:]
        myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value), subLabels)
    return myTree

myDat,labels = CreateDataSet()
print(calcShannonEnt(myDat))

print(splitDataSet(myDat, 1, 1))

print(chooseBestFeatureToSplit(myDat))

print(createTree(myDat, labels))