• 吴裕雄--天生自然python机器学习:使用决策树预测隐形眼镜类型


    解决策树如何预测患者需要佩戴的隐形眼镜类型。使用小数据
    集,我们就可以利用决策树学到很多知识:眼科医生是如何判断患者需要佩戴的镜片类型;一旦
    理解了决策树的工作原理,我们甚至也可以帮助人们判断需要佩戴的镜片类型。

     

     

    隐 形 眼 镜 数 据 集 是 非 常 著 名 的 数 据 集 ,它 包 含 很 多 患 者 眼 部 状 况 的 观 察 条 件 以 及 医 生 推 荐 的
    隐 形 眼 镜 类 型 。隐 形 眼 镜 类 型 包 括 硬 材 质 、软 材 质 以 及 不 适 合 佩 戴 隐 形 眼 镜 。数 据 来 源 于UCI 数 据
    库。

    import numpy as np
    import operator as op
    from math import log
    
    def calcShannonEnt(dataSet):
        labelCounts = {}
        for featVec in dataSet: 
            currentLabel = featVec[-1]
            if(currentLabel not in labelCounts.keys()): 
                labelCounts[currentLabel] = 0
            labelCounts[currentLabel] += 1
        shannonEnt = 0.0
        rowNum = len(dataSet)
        for key in labelCounts:
            prob = float(labelCounts[key])/rowNum
            shannonEnt -= prob * log(prob,2)
        return shannonEnt
    
    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 = np.shape(dataSet)[1]-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.items(), key=op.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
    
    def classify(inputTree,featLabels,testVec):
        for i in inputTree.keys():
            firstStr = i
            break
        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 classLabel
    
    data = open("F:\machinelearninginaction\Ch03\lenses.txt")
    dataSet = [inst.strip().split("	") for inst in data.readlines()]
    print(dataSet)
    print(np.shape(dataSet))
    labels = ["age","prescript","astigmatic","tearRate"]
    tree = createTree(dataSet,labels)
    print(tree)
    
    import matplotlib.pyplot as plt
    
    decisionNode = dict(boxstyle="sawtooth", fc="0.8")
    leafNode = dict(boxstyle="round4", fc="0.8")
    arrow_args = dict(arrowstyle="<-")
    
    def getNumLeafs(myTree):
        numLeafs = 0
        for i in myTree.keys():
            firstStr = i
            break
        secondDict = myTree[firstStr]
        for key in secondDict.keys():
            if type(secondDict[key]).__name__=='dict':
                numLeafs += getNumLeafs(secondDict[key])
            else:   numLeafs +=1
        return numLeafs
    
    def getTreeDepth(myTree):
        maxDepth = 0
        for i in myTree.keys():
            firstStr = i
            break
        secondDict = myTree[firstStr]
        for key in secondDict.keys():
            if type(secondDict[key]).__name__=='dict':
                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):
        numLeafs = getNumLeafs(myTree)  
        depth = getTreeDepth(myTree)
        for i in myTree.keys():
            firstStr = i
            break
        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': 
                plotTree(secondDict[key],cntrPt,str(key))     
            else:  
                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
        
    def createPlot(inTree):
        fig = plt.figure(1, facecolor='white')
        fig.clf()
        axprops = dict(xticks=[], yticks=[])
        createPlot.ax1 = plt.subplot(111, frameon=False, **axprops)    
        #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()
    
    createPlot(tree)

     可 以 发 现 ,医 生 最 多 需 要 问 四 个 问 题 就 能 确 定 患 者 需 要 佩 戴 哪 种 类 型 的 隐 形 眼 镜 。

    小结:

     

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  • 原文地址:https://www.cnblogs.com/tszr/p/12040829.html
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