读书笔记：机器学习实战(2)——章3的决策树代码和个人理解与注释

首先是对于决策树的个人理解：
通过寻找最大信息增益（或最小信息熵）的分类特征，从部分已知类别的数据中提取分类规则的一种分类方法。
信息熵：

其中，log底数为2，额，好吧，图片我从百度截的。。
这里只解释到它是一种信息的期望值，深入的请看维基百科

http://zh.wikipedia.org/zh-sg/熵_(信息论)

信息增益：划分数据集前后的信息发生的变化（原书定义）
实际应用想要找到具有最大信息增益的分类树结构，就是使原始数据的信息熵减去分类后的信息熵的差值最大，原始数据的信息熵可以理解为常数，那么想要最大信息增益，也就是要寻找一种分类方法，是按照分类方法分类后的数据集的信息熵最小。（另：也可以选择“不纯度”或“错误率”作为评估参数，不纯度，维基百科下，错误率就是字面意思）
所以找到最优分类树结构代码如下：

def chooseBestFeatureToSplit(dataSet):    numFeatures = len(dataSet[0]) - 1      #the last column is used for the labels    baseEntropy = calcShannonEnt(dataSet)    bestInfoGain = 0.0; bestFeature = -1    for i in range(numFeatures):        #iterate over all the features        featList = [example[i] for example in dataSet]#create a list of all the examples of this feature        uniqueVals = set(featList)       #get a set of unique values        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     #calculate the info gain; ie reduction in entropy        if (infoGain > bestInfoGain):       #compare this to the best gain so far            bestInfoGain = infoGain         #if better than current best, set to best            bestFeature = i    return bestFeature     

按照“寻找最大信息增益的方式”，找到对于已知类别的一批数据（训练集）的最优决策树，然后用这个树结构去分类未知数据（测试集），整体代码如下：

#!/usr/bin/env python# coding=utf-8'''Created on Oct 12, 2010Decision Tree Source Code for Machine Learning in Action Ch. 3@author: Peter Harrington'''from math import logimport operatordef createDataSet():    dataSet = [[1, 1, 'yes'],               [1, 1, 'yes'],               [1, 0, 'no'],               [0, 1, 'no'],               [0, 1, 'no']]    labels = ['no surfacing','flippers']    #change to discrete values    return dataSet, labelsdef calcShannonEnt(dataSet):    # 计算香侬熵    numEntries = len(dataSet)    labelCounts = {}    # 存储特征的字典    for featVec in dataSet: #the the number of unique elements and their occurance        currentLabel = featVec[-1]        # 取最后一个元素，即该组特征的label        if currentLabel not in labelCounts.keys(): labelCounts[currentLabel] = 0        # 如果没有，增加新key，value初始化为0        labelCounts[currentLabel] += 1        # 对应key的值累计    shannonEnt = 0.0    for key in labelCounts:        prob = float(labelCounts[key])/numEntries        shannonEnt -= prob * log(prob,2) #log base 2        # shannon公式：shanonEnt ＝（负的）求和（i.prob*log(i.prob,2))    return shannonEntdef splitDataSet(dataSet, axis, value):    retDataSet = []    for featVec in dataSet:        if featVec[axis] == value:            reducedFeatVec = featVec[:axis]     #chop out axis used for splitting            reducedFeatVec.extend(featVec[axis+1:])            # 简单的分片，除去分类特征，余下的添加到容器中            retDataSet.append(reducedFeatVec)    return retDataSetdef chooseBestFeatureToSplit(dataSet):    numFeatures = len(dataSet[0]) - 1      #the last column is used for the labels    baseEntropy = calcShannonEnt(dataSet)    bestInfoGain = 0.0; bestFeature = -1    for i in range(numFeatures):        #iterate over all the features        featList = [example[i] for example in dataSet]#create a list of all the examples of this feature        uniqueVals = set(featList)       #get a set of unique values        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     #calculate the info gain; ie reduction in entropy        if (infoGain > bestInfoGain):       #compare this to the best gain so far            bestInfoGain = infoGain         #if better than current best, set to best            bestFeature = i    return bestFeature                      #returns an integerdef 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]#stop splitting when all of the classes are equal    if len(dataSet[0]) == 1: #stop splitting when there are no more features in dataSet        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[:]       #copy all of labels, so trees don't mess up existing labels        myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value),subLabels)    return myTree                            def classify(inputTree,featLabels,testVec):    firstStr = inputTree.keys()[0]    secondDict = inputTree[firstStr]    featIndex = featLabels.index(firstStr)    key = testVec[featIndex]    valueOfFeat = secondDict[key]    if isinstance(valueOfFeat, dict):         classLabel = classify(valueOfFeat, featLabels, testVec)    else: classLabel = valueOfFeat    return classLabeldef 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)if __name__ == '__main__':    (dataSet, labels) = createDataSet();    print dataSet;print labels    shannonEnt = calcShannonEnt(dataSet)    print shannonEnt    myTree = createTree(dataSet,labels )    print 'mytree:'    print myTree    (dataSet, labels) = createDataSet();    print classify(myTree,labels, [1,1])    import treePlotter    # treePlotter.createPlot(myTree)    fr = open('lenses.txt')    lenses = [inst.strip().split('\t') for inst in fr.readlines()]    lensesLabels = ['age','prescript','astigmatic','tearRate']    lensesTree = createTree(lenses,lensesLabels)    print lensesTree    treePlotter.createPlot(lensesTree)

部分地方加入了中文注释，原著的那几行英文注释很好就没有再换成中文的。
之前没有详细看过决策树，以为它就是把分类逻辑变为树结构，多个if else，说说个人学习后，对于决策树的理解：
1.还是觉得它就是多个if else，树结构也可以这么理解吧
2.构建的过程或者说收敛条件是：最大信息增益（最小信息熵）
3.优点：可读性强，逻辑简单易懂，计算步骤不超过树的深度；缺点：极易过拟合，得到的树结构泛性不强
4.正因为过度追求最优解，导致决策树往往会过拟合，原著是通过构建以后的合并细小或相近分支，也就是“后置裁剪”，但是这样时间上有浪费，代表的是K-Fold Cross Validation，不断裁剪，评估当前的错误率，有点类似于整体求解后，再反过来找恰当的“early stop”；另一种就是著名的随机森林，系统复杂了，效果确实会好，额，随机森林具体的以后深度学习下补上。
下面是原著利用matplolib画图的代码：

'''Created on Oct 14, 2010@author: Peter Harrington'''import matplotlib.pyplot as pltdecisionNode = dict(boxstyle="sawtooth", fc="0.8")leafNode = dict(boxstyle="round4", fc="0.8")arrow_args = dict(arrowstyle="<-")def getNumLeafs(myTree):    numLeafs = 0    firstStr = myTree.keys()[0]    secondDict = myTree[firstStr]    for key in secondDict.keys():        if type(secondDict[key]).__name__=='dict':#test to see if the nodes are dictonaires, if not they are leaf nodes            numLeafs += getNumLeafs(secondDict[key])        else:   numLeafs +=1    return numLeafsdef getTreeDepth(myTree):    maxDepth = 0    firstStr = myTree.keys()[0]    secondDict = myTree[firstStr]    for key in secondDict.keys():        if type(secondDict[key]).__name__=='dict':#test to see if the nodes are dictonaires, if not they are leaf nodes            thisDepth = 1 + getTreeDepth(secondDict[key])        else:   thisDepth = 1        if thisDepth > maxDepth: maxDepth = thisDepth    return maxDepthdef plotNode(nodeTxt, centerPt, parentPt, nodeType):    createPlot.ax1.annotate(nodeTxt, xy=parentPt,  xycoords='axes fraction',             xytext=centerPt, textcoords='axes fraction',             va="center", ha="center", bbox=nodeType, arrowprops=arrow_args )def plotMidText(cntrPt, parentPt, txtString):    xMid = (parentPt[0]-cntrPt[0])/2.0 + cntrPt[0]    yMid = (parentPt[1]-cntrPt[1])/2.0 + cntrPt[1]    createPlot.ax1.text(xMid, yMid, txtString, va="center", ha="center", rotation=30)def plotTree(myTree, parentPt, nodeTxt):#if the first key tells you what feat was split on    numLeafs = getNumLeafs(myTree)  #this determines the x width of this tree    depth = getTreeDepth(myTree)    firstStr = myTree.keys()[0]     #the text label for this node should be this    cntrPt = (plotTree.xOff + (1.0 + float(numLeafs))/2.0/plotTree.totalW, plotTree.yOff)    plotMidText(cntrPt, parentPt, nodeTxt)    plotNode(firstStr, cntrPt, parentPt, decisionNode)    secondDict = myTree[firstStr]    plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD    for key in secondDict.keys():        if type(secondDict[key]).__name__=='dict':#test to see if the nodes are dictonaires, if not they are leaf nodes               plotTree(secondDict[key],cntrPt,str(key))        #recursion        else:   #it's a leaf node print the leaf node            plotTree.xOff = plotTree.xOff + 1.0/plotTree.totalW            plotNode(secondDict[key], (plotTree.xOff, plotTree.yOff), cntrPt, leafNode)            plotMidText((plotTree.xOff, plotTree.yOff), cntrPt, str(key))    plotTree.yOff = plotTree.yOff + 1.0/plotTree.totalD#if you do get a dictonary you know it's a tree, and the first element will be another dictdef createPlot(inTree):    fig = plt.figure(1, facecolor='white')    fig.clf()    axprops = dict(xticks=[], yticks=[])    createPlot.ax1 = plt.subplot(111, frameon=False, **axprops)    #no ticks    #createPlot.ax1 = plt.subplot(111, frameon=False) #ticks for demo puropses     plotTree.totalW = float(getNumLeafs(inTree))    plotTree.totalD = float(getTreeDepth(inTree))    plotTree.xOff = -0.5/plotTree.totalW; plotTree.yOff = 1.0;    plotTree(inTree, (0.5,1.0), '')    plt.show()#def createPlot():#    fig = plt.figure(1, facecolor='white')#    fig.clf()#    createPlot.ax1 = plt.subplot(111, frameon=False) #ticks for demo puropses #    plotNode('a decision node', (0.5, 0.1), (0.1, 0.5), decisionNode)#    plotNode('a leaf node', (0.8, 0.1), (0.3, 0.8), leafNode)#    plt.show()def retrieveTree(i):    listOfTrees =[{'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}},                  {'no surfacing': {0: 'no', 1: {'flippers': {0: {'head': {0: 'no', 1: 'yes'}}, 1: 'no'}}}}                  ]    return listOfTrees[i]#createPlot(thisTree)