python关于豆瓣图书决策树构建

Matplotlib绘制决策树代码：

            
              # coding=utf-8
import matplotlib.pyplot as plt
'''
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'''
decisionNode = dict(boxstyle='sawtooth', fc='10')
leafNode = dict(boxstyle='round4',fc='0.8')
arrow_args = dict(arrowstyle='<-')



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 getNumLeafs(myTree):
    numLeafs = 0
    firstStr = list(myTree.keys())[0]
    secondDict = myTree[firstStr]
    for key in secondDict:
        if(type(secondDict[key]).__name__ == 'dict'):
            numLeafs += getNumLeafs(secondDict[key])
        else:
            numLeafs += 1
    return  numLeafs

def getTreeDepth(myTree):
    maxDepth = 0
    firstStr = list(myTree.keys())[0]
    secondDict = myTree[firstStr]
    for key in secondDict:
        if(type(secondDict[key]).__name__ == 'dict'):
            thisDepth = 1+getTreeDepth((secondDict[key]))
        else:
            thisDepth = 1
        if thisDepth > maxDepth: maxDepth = thisDepth
    return maxDepth

def retrieveTree(i):
    #预先设置树的信息
    listOfTree = [{'no surfacing':{0:'no',1:{'flipper':{0:'no',1:'yes'}}}},
        {'no surfacing':{0:'no',1:{'flipper':{0:{'head':{0:'no',1:'yes'}},1:'no'}}}},
        {'Comment score greater than 8.0':{0:{'Comment score greater than 9.5':{0:'Yes',1:{'More than 45,000 people commented': {
        0: 'Yes',1: 'No'}}}},1:'No'}}]
    return listOfTree[i]

def createPlot(inTree):
    fig = plt.figure(1,facecolor='white')
    fig.clf()
    axprops = dict(xticks = [], yticks=[])
    createPlot.ax1 = plt.subplot(111,frameon = False,**axprops)
    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.title('Douban reading Decision Tree\n')
    plt.show()

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)

def plotTree(myTree, parentPt, nodeTxt):
    numLeafs = getNumLeafs(myTree)
    depth = getTreeDepth(myTree)
    firstStr = list(myTree.keys())[0]
    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:
        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

if __name__ == '__main__':
    myTree = retrieveTree(2)
    createPlot(myTree)

            
          

运行结果：