上篇中，實現了創建決策樹但并不直觀，這里學習繪制決策樹，便于直觀理解。

Matplotlib提供了名為pylab的模塊，其中包括了許多numpy和pyplot中常用的函數，方便用戶快速進行計算和繪圖，

可以用于IPython中的快速交互式使用。

Matplotlib中的快速繪圖的函數庫可以通過如下語句載入：

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import?matplotlib.pyplot?as?plt??

import matplotlib.pyplot as plt繪制樹形圖，我們需要定義樹和葉的形態，還必須要知道有多少個葉節點和判斷節點，還有樹的層數，這樣才能確定樹的大小，繪制繪圖區

首先注解繪制的樹節點和葉節點以及箭頭

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#定義文本框和箭頭格式??

decisionNode?=?dict(boxstyle=”sawtooth”,?fc=“0.8”)?#定義判斷節點形態??

leafNode?=?dict(boxstyle=”round4”,?fc=“0.8”)?#定義葉節點形態??

arrow_args?=?dict(arrowstyle=”<-“)?#定義箭頭??

??

#繪制帶箭頭的注解??

#nodeTxt：節點的文字標注,?centerPt：節點中心位置,??

#parentPt：箭頭起點位置（上一節點位置）,?nodeType：節點屬性??

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?)??

#定義文本框和箭頭格式
decisionNode = dict(boxstyle=”sawtooth”, fc=”0.8”) #定義判斷節點形態
leafNode = dict(boxstyle=”round4”, fc=”0.8”) #定義葉節點形態
arrow_args = dict(arrowstyle=”<-“) #定義箭頭

#繪制帶箭頭的注解 #nodeTxt：節點的文字標注, centerPt：節點中心位置, #parentPt：箭頭起點位置（上一節點位置）, nodeType：節點屬性 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 )

然后得到葉節點的數目和樹的層數

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#計算葉節點數??

def?getNumLeafs(myTree):??

????numLeafs?=?0??

????firstStr?=?myTree.keys()[0]???

????secondDict?=?myTree[firstStr]???

????for?key?in?secondDict.keys():??

????????if?type(secondDict[key]).__name__==‘dict’:#是否是字典??

????????????numLeafs?+=?getNumLeafs(secondDict[key])?#遞歸調用getNumLeafs??

????????else:???numLeafs?+=1?#如果是葉節點，則葉節點+1??

????return?numLeafs??

??

#計算數的層數??

def?getTreeDepth(myTree):??

????maxDepth?=?0??

????firstStr?=?myTree.keys()[0]??

????secondDict?=?myTree[firstStr]??

????for?key?in?secondDict.keys():??

????????if?type(secondDict[key]).__name__==‘dict’:#是否是字典??

????????????thisDepth?=?1?+?getTreeDepth(secondDict[key])?#如果是字典，則層數加1，再遞歸調用getTreeDepth??

????????else:???thisDepth?=?1??

????????#得到最大層數??

????????if?thisDepth?>?maxDepth:??

????????????maxDepth?=?thisDepth??

????return?maxDepth??

#計算葉節點數 def getNumLeafs(myTree):numLeafs = 0firstStr = myTree.keys()[0] secondDict = myTree[firstStr] for key in secondDict.keys():if type(secondDict[key]).__name__=='dict':#是否是字典numLeafs += getNumLeafs(secondDict[key]) #遞歸調用getNumLeafselse: numLeafs +=1 #如果是葉節點，則葉節點+1return numLeafs #計算數的層數 def getTreeDepth(myTree): maxDepth = 0 firstStr = myTree.keys()[0] secondDict = myTree[firstStr] for key in secondDict.keys(): if type(secondDict[key]).__name__=='dict':#是否是字典 thisDepth = 1 + getTreeDepth(secondDict[key]) #如果是字典，則層數加1，再遞歸調用getTreeDepth else: thisDepth = 1 #得到最大層數 if thisDepth > maxDepth: maxDepth = thisDepth return maxDepth有了注解和計算樹形圖的位置的參數，就可以繪制樹形圖了

為了清晰簡明，在父子節點之間加入文本標簽信息

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#在父子節點間填充文本信息??

#cntrPt:子節點位置,?parentPt：父節點位置,?txtString：標注內容??

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)??

#在父子節點間填充文本信息 #cntrPt:子節點位置, parentPt：父節點位置, txtString：標注內容 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)然后繪制樹形圖；

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#繪制樹形圖??

#myTree：樹的字典,?parentPt:父節點,?nodeTxt：節點的文字標注??

def?plotTree(myTree,?parentPt,?nodeTxt):??

????numLeafs?=?getNumLeafs(myTree)??#樹葉節點數??

????depth?=?getTreeDepth(myTree)????#樹的層數??

????firstStr?=?myTree.keys()[0]?????#節點標簽??

????#計算當前節點的位置??

????cntrPt?=?(plotTree.xOff?+?(1.0?+?float(numLeafs))/2.0/plotTree.totalW,?plotTree.yOff)??

????plotMidText(cntrPt,?parentPt,?nodeTxt)?#在父子節點間填充文本信息??

????plotNode(firstStr,?cntrPt,?parentPt,?decisionNode)?#繪制帶箭頭的注解??

????secondDict?=?myTree[firstStr]??

????plotTree.yOff?=?plotTree.yOff?-?1.0/plotTree.totalD??

????for?key?in?secondDict.keys():??

????????if?type(secondDict[key]).__name__==‘dict’:#判斷是不是字典，??

????????????plotTree(secondDict[key],cntrPt,str(key))????????#遞歸繪制樹形圖??

????????else:???#如果是葉節點??

????????????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??

??

#創建繪圖區??

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))?#樹的寬度??

????plotTree.totalD?=?float(getTreeDepth(inTree))?#樹的深度??

????plotTree.xOff?=?-0.5/plotTree.totalW;?plotTree.yOff?=?1.0;??

????plotTree(inTree,?(0.5,1.0),?”)??

????plt.show()??

#繪制樹形圖 #myTree：樹的字典, parentPt:父節點, nodeTxt：節點的文字標注 def plotTree(myTree, parentPt, nodeTxt): numLeafs = getNumLeafs(myTree) #樹葉節點數 depth = getTreeDepth(myTree) #樹的層數 firstStr = myTree.keys()[0] #節點標簽 #計算當前節點的位置 cntrPt = (plotTree.xOff + (1.0 + float(numLeafs))/2.0/plotTree.totalW, plotTree.yOff) plotMidText(cntrPt, parentPt, nodeTxt) #在父子節點間填充文本信息 plotNode(firstStr, cntrPt, parentPt, decisionNode) #繪制帶箭頭的注解 secondDict = myTree[firstStr] plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD for key in secondDict.keys(): if type(secondDict[key]).__name__=='dict':#判斷是不是字典， plotTree(secondDict[key],cntrPt,str(key)) #遞歸繪制樹形圖 else: #如果是葉節點 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 #創建繪圖區 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)) #樹的寬度 plotTree.totalD = float(getTreeDepth(inTree)) #樹的深度 plotTree.xOff = -0.5/plotTree.totalW; plotTree.yOff = 1.0; plotTree(inTree, (0.5,1.0), '') plt.show()其中createPlot（）是主函數，創建繪圖區，計算樹形圖的尺寸大小，它調用plotTree（）等函數，plotTree（）遞歸畫出整個樹形圖。

加載之前創建了tree模塊和這個treeplot模塊，在命令提示符下輸入

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>>>?import?treeplot??

>>>?import?tree??

>>>?myDat,labels?=?tree.createDataSet()??

>>>?myTree?=?tree.createTree(myDat,labels)??

>>>?treeplot.createPlot(myTree)??

>>> import treeplot >>> import tree >>> myDat,labels = tree.createDataSet() >>> myTree = tree.createTree(myDat,labels) >>> treeplot.createPlot(myTree)得到正確的樹形圖

用創建的tree模塊和treeplot模塊，使用決策樹預測隱形眼鏡類型；

在命令提示符下輸入

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>>>?import?tree??

>>>?import?treeplot??

>>>?fr?=?open(’lenses.txt’)??

>>>?lenses?=?[inst.strip().split(’\t’)?for?inst?in?fr.readlines()]??

>>>?lensesLabels?=?[’age’,‘prescript’,‘astigmatic’,‘tearRate’]??

>>>?lensesTree?=?tree.createTree(lenses,lensesLabels)??

>>>?treeplot.createPlot(lensesTree)??

>>> import tree >>> import treeplot >>> fr = open('lenses.txt') >>> lenses = [inst.strip().split('\t') for inst in fr.readlines()] >>> lensesLabels = ['age','prescript','astigmatic','tearRate'] >>> lensesTree = tree.createTree(lenses,lensesLabels) >>> treeplot.createPlot(lensesTree)得到如下所示

如有不足，請指出，謝謝~~

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