机器学习之决策树二-C4.5原理与代码实现

决策树之系列二—C4.5原理与代码实现

本文系作者原创，转载请注明出处:https://www.cnblogs.com/further-further-further/p/9435712.html

ID3算法缺点

它一般会优先选择有较多属性值的Feature，因为属性值多的特征会有相对较大的信息增益，信息增益反映的是，在给定一个条件以后，不确定性减少的程度，

这必然是分得越细的数据集确定性更高，也就是条件熵越小，信息增益越大。为了解决这个问题，C4.5就应运而生，它采用信息增益率来作为选择分支的准则。

C4.5算法原理

信息增益率定义为：

其中，分子为信息增益（信息增益计算可参考上一节ID3的算法原理），分母为属性X的熵。

需要注意的是，增益率准则对可取值数目较少的属性有所偏好。

所以一般这样选取划分属性：选择增益率最高的特征列作为划分属性的依据。

代码实现

与ID3代码实现不同的是：只改变计算香农熵的函数calcShannonEnt，以及选择最优特征索引函数chooseBestFeatureToSplit，具体代码如下：

 # -*- coding: utf-8 -*-
 """
 Created on Thu Aug  2 17:09:34 2018
 决策树ID3,C4.5的实现
 @author: weixw
 """
 from math import log
 import operator
 #原始数据
 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
 
 #多数表决器
 #列中相同值数量最多为结果
 def majorityCnt(classList):
     classCounts = {}
     for value in classList:
         if(value not in classCounts.keys()):
             classCounts[value] = 0
         classCounts[value] +=1
     sortedClassCount = sorted(classCounts.iteritems(),key = operator.itemgetter(1),reverse =True)
     return sortedClassCount[0][0]
 
 #划分数据集
 #dataSet:原始数据集
 #axis:进行分割的指定列索引
 #value:指定列中的值
 def splitDataSet(dataSet,axis,value):
     retDataSet= []
     for featDataVal in dataSet:
         if featDataVal[axis] == value:
             #下面两行去除某一项指定列的值，很巧妙有没有
             reducedFeatVal = featDataVal[:axis]
             reducedFeatVal.extend(featDataVal[axis+1:])
             retDataSet.append(reducedFeatVal)
     return retDataSet
 
 #计算香农熵
 #columnIndex = -1表示获取数据集每一项的最后一列的标签值
 #其他表示获取特征列
 def calcShannonEnt(columnIndex, dataSet):
     #数据集总项数
     numEntries = len(dataSet)
     #标签计数对象初始化
     labelCounts = {}
     for featDataVal in dataSet:
         #获取数据集每一项的最后一列的标签值
         currentLabel = featDataVal[columnIndex]
         #如果当前标签不在标签存储对象里，则初始化，然后计数
         if currentLabel not in labelCounts.keys():
             labelCounts[currentLabel] = 0
         labelCounts[currentLabel] += 1
     #熵初始化
     shannonEnt = 0.0
     #遍历标签对象，求概率，计算熵
     for key in labelCounts.keys():
         prop = labelCounts[key]/float(numEntries)
         shannonEnt -= prop*log(prop,2)
     return shannonEnt
 
 #通过信息增益，选出最优特征列索引(ID3)
 def chooseBestFeatureToSplit(dataSet):
     #计算特征个数，dataSet最后一列是标签属性，不是特征量
     numFeatures = len(dataSet[0])-1
     #计算初始数据香农熵
     baseEntropy = calcShannonEnt(-1, dataSet)
     #初始化信息增益，最优划分特征列索引
     bestInfoGain = 0.0
     bestFeatureIndex = -1
     for i in range(numFeatures):
         #获取每一列数据
         featList = [example[i] for example in dataSet]
         #将每一列数据去重
         uniqueVals = set(featList)
         newEntropy = 0.0
         for value in uniqueVals:
             subDataSet = splitDataSet(dataSet,i,value)
             #计算条件概率
             prob = len(subDataSet)/float(len(dataSet))
             #计算条件熵
             newEntropy +=prob*calcShannonEnt(-1, subDataSet)
         #计算信息增益
         infoGain = baseEntropy - newEntropy
         if(infoGain > bestInfoGain):
             bestInfoGain = infoGain
             bestFeatureIndex = i
     return bestFeatureIndex
 
 #通过信息增益率，选出最优特征列索引(C4.5)
 def chooseBestFeatureToSplitOfFurther(dataSet):
     #计算特征个数，dataSet最后一列是标签属性，不是特征量
     numFeatures = len(dataSet[0])-1
     #计算初始数据香农熵H(Y)
     baseEntropy = calcShannonEnt(-1, dataSet)
     #初始化信息增益，最优划分特征列索引
     bestInfoGainRatio = 0.0
     bestFeatureIndex = -1
     for i in range(numFeatures):
         #获取每一特征列香农熵H(X)
         featEntropy = calcShannonEnt(i, dataSet)
         #获取每一列数据
         featList = [example[i] for example in dataSet]
         #将每一列数据去重
         uniqueVals = set(featList)
         newEntropy = 0.0
         for value in uniqueVals:
             subDataSet = splitDataSet(dataSet,i,value)
             #计算条件概率
             prob = len(subDataSet)/float(len(dataSet))
             #计算条件熵
             newEntropy +=prob*calcShannonEnt(-1, subDataSet)
         #计算信息增益
         infoGain = baseEntropy - newEntropy
         #计算信息增益率
         infoGainRatio = infoGain/float(featEntropy)
         if(infoGainRatio > bestInfoGainRatio):
             bestInfoGainRatio = infoGainRatio
             bestFeatureIndex = i
     return bestFeatureIndex
 
 #决策树创建
 def createTree(dataSet,labels):
     #获取标签属性，dataSet最后一列，区别于labels标签名称
     classList = [example[-1] for example in dataSet]
     #树极端终止条件判断
     #标签属性值全部相同，返回标签属性第一项值
     if classList.count(classList[0]) == len(classList):
         return classList[0]
     #没有特征，只有标签列（1列）
     if len(dataSet[0]) == 1:
         #返回实例数最大的类
         return majorityCnt(classList)
 #    #获取最优特征列索引ID3
 #    bestFeatureIndex = chooseBestFeatureToSplit(dataSet)
     #获取最优特征列索引C4.5
     bestFeatureIndex = chooseBestFeatureToSplitOfFurther(dataSet)
     #获取最优索引对应的标签名称
     bestFeatureLabel = labels[bestFeatureIndex]
     #创建根节点
     myTree = {bestFeatureLabel:{}}
     #去除最优索引对应的标签名，使labels标签能正确遍历
     del(labels[bestFeatureIndex])
     #获取最优列
     bestFeature = [example[bestFeatureIndex] for example in dataSet]
     uniquesVals = set(bestFeature)
     for value in uniquesVals:
         #子标签名称集合
         subLabels = labels[:]
         #递归
         myTree[bestFeatureLabel][value] = createTree(splitDataSet(dataSet,bestFeatureIndex,value),subLabels)
     return myTree
 
 #获取分类结果
 #inputTree:决策树字典
 #featLabels:标签列表
 #testVec:测试向量  例如：简单实例下某一路径 [1,1]  => yes（树干值组合，从根结点到叶子节点）
 def classify(inputTree,featLabels,testVec):
     #获取根结点名称，将dict转化为list
     firstSide = list(inputTree.keys())
     #根结点名称String类型
     firstStr = firstSide[0]
     #获取根结点对应的子节点
     secondDict = inputTree[firstStr]
     #获取根结点名称在标签列表中对应的索引
     featIndex = featLabels.index(firstStr)
     #由索引获取向量表中的对应值
     key = testVec[featIndex]
     #获取树干向量后的对象
     valueOfFeat = secondDict[key]
     #判断是子结点还是叶子节点：子结点就回调分类函数，叶子结点就是分类结果
     #if type(valueOfFeat).__name__=='dict': 等价 if isinstance(valueOfFeat, dict):
     if isinstance(valueOfFeat, dict):
         classLabel = classify(valueOfFeat,featLabels,testVec)
     else:
         classLabel = valueOfFeat
     return classLabel
 
 #将决策树分类器存储在磁盘中，filename一般保存为txt格式
 def storeTree(inputTree,filename):
     import pickle
     fw = open(filename,'wb+')
     pickle.dump(inputTree,fw)
     fw.close()
 #将瓷盘中的对象加载出来，这里的filename就是上面函数中的txt文件
 def grabTree(filename):
     import pickle
     fr = open(filename,'rb')
     return pickle.load(fr)

决策树算法

 '''
 Created on Oct 14, 2010
 
 @author: Peter Harrington
 '''
 import matplotlib.pyplot as plt
 
 decisionNode = dict(boxstyle="sawtooth", fc="0.8")
 leafNode = dict(boxstyle="round4", fc="0.8")
 arrow_args = dict(arrowstyle="<-")
 
 #获取树的叶子节点
 def getNumLeafs(myTree):
     numLeafs = 0
     #dict转化为list
     firstSides = list(myTree.keys())
     firstStr = firstSides[0]
     secondDict = myTree[firstStr]
     for key in secondDict.keys():
         #判断是否是叶子节点（通过类型判断，子类不存在，则类型为str；子类存在，则为dict）
         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 numLeafs
 
 #获取树的层数
 def getTreeDepth(myTree):
     maxDepth = 0
     #dict转化为list
     firstSides = list(myTree.keys())
     firstStr = firstSides[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 maxDepth
 
 def 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)
     firstSides = list(myTree.keys())
     firstStr = firstSides[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 dict
 #绘制决策树
 def 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()
 
 #绘制树的根节点和叶子节点（根节点形状：长方形，叶子节点：椭圆形）
 #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]
 
 #thisTree = retrieveTree(0)
 #createPlot(thisTree)
 #createPlot()
 #myTree = retrieveTree(0)
 #numLeafs =getNumLeafs(myTree)
 #treeDepth =getTreeDepth(myTree)
 #print(u"叶子节点数目：%d"% numLeafs)
 #print(u"树深度：%d"%treeDepth)

绘制决策树

 # -*- coding: utf-8 -*-
 """
 Created on Fri Aug  3 19:52:10 2018
 
 @author: weixw
 """
 import myTrees as mt
 import treePlotter as tp
 #测试
 dataSet, labels = mt.createDataSet()
 #copy函数：新开辟一块内存，然后将list的所有值复制到新开辟的内存中
 labels1 = labels.copy()
 #createTree函数中将labels1的值改变了，所以在分类测试时不能用labels1
 myTree = mt.createTree(dataSet,labels1)
 #保存树到本地
 mt.storeTree(myTree,'myTree.txt')
 #在本地磁盘获取树
 myTree = mt.grabTree('myTree.txt')
 print(u"采用C4.5算法的决策树结果")
 print (u"决策树结构：%s"%myTree)
 #绘制决策树
 print(u"绘制决策树：")
 tp.createPlot(myTree)
 numLeafs =tp.getNumLeafs(myTree)
 treeDepth =tp.getTreeDepth(myTree)
 print(u"叶子节点数目：%d"% numLeafs)
 print(u"树深度：%d"%treeDepth)
 #测试分类 简单样本数据3列
 labelResult =mt.classify(myTree,labels,[1,1])
 print(u"[1,1] 测试结果为：%s"%labelResult)
 labelResult =mt.classify(myTree,labels,[1,0])
 print(u"[1,0] 测试结果为：%s"%labelResult)

测试

运行结果

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