Python爬虫(二)——豆瓣图书决策树构建
前文参考：https://www.cnblogs.com/LexMoon/p/douban1.html Matplotlib绘制决策树代码： 1 # coding=utf-8 2 import matplotlib.pyplot as plt 3 4 decisionNode = dict(boxstyle='sawtooth', fc='10') 5 leafNode = dict(boxstyle='round4',fc='0.8') 6 arrow_args = dict(arrowstyle='<-') 7 8 9 10 def plotNode(nodeTxt, centerPt, parentPt, nodeType): 11 createPlot.ax1.annotate(nodeTxt, xy=parentPt, xycoords='axes fraction',\ 12 xytext=centerPt,textcoords='axes fraction',\ 13 va='center', ha='center',bbox=nodeType,arrowprops\ 14 =arrow_args) 15 16 17 def getNumLeafs(myTree): 18 numLeafs = 0 19 firstStr = list(myTree.keys())[0] 20 secondDict = myTree[firstStr] 21 for key in secondDict: 22 if(type(secondDict[key]).__name__ == 'dict'): 23 numLeafs += getNumLeafs(secondDict[key]) 24 else: 25 numLeafs += 1 26 return numLeafs 27 28 def getTreeDepth(myTree): 29 maxDepth = 0 30 firstStr = list(myTree.keys())[0] 31 secondDict = myTree[firstStr] 32 for key in secondDict: 33 if(type(secondDict[key]).__name__ == 'dict'): 34 thisDepth = 1+getTreeDepth((secondDict[key])) 35 else: 36 thisDepth = 1 37 if thisDepth > maxDepth: maxDepth = thisDepth 38 return maxDepth 39 40 def retrieveTree(i): 41 #预先设置树的信息 42 listOfTree = [{'no surfacing':{0:'no',1:{'flipper':{0:'no',1:'yes'}}}}, 43 {'no surfacing':{0:'no',1:{'flipper':{0:{'head':{0:'no',1:'yes'}},1:'no'}}}}, 44 {'Comment score greater than 8.0':{0:{'Comment score greater than 9.5':{0:'Yes',1:{'More than 45,000 people commented': { 45 0: 'Yes',1: 'No'}}}},1:'No'}}] 46 return listOfTree[i] 47 48 def createPlot(inTree): 49 fig = plt.figure(1,facecolor='white') 50 fig.clf() 51 axprops = dict(xticks = [], yticks=[]) 52 createPlot.ax1 = plt.subplot(111,frameon = False,**axprops) 53 plotTree.totalW = float(getNumLeafs(inTree)) 54 plotTree.totalD = float(getTreeDepth(inTree)) 55 plotTree.xOff = -0.5/plotTree.totalW;plotTree.yOff = 1.0 56 plotTree(inTree,(0.5,1.0), '') 57 plt.title('Douban reading Decision Tree\n') 58 plt.show() 59 60 def plotMidText(cntrPt, parentPt,txtString): 61 xMid = (parentPt[0]-cntrPt[0])/2.0 + cntrPt[0] 62 yMid = (parentPt[1] - cntrPt[1])/2.0 + cntrPt[1] 63 createPlot.ax1.text(xMid, yMid, txtString) 64 65 def plotTree(myTree, parentPt, nodeTxt): 66 numLeafs = getNumLeafs(myTree) 67 depth = getTreeDepth(myTree) 68 firstStr = list(myTree.keys())[0] 69 cntrPt = (plotTree.xOff+(1.0+float(numLeafs))/2.0/plotTree.totalW,\ 70 plotTree.yOff) 71 plotMidText(cntrPt,parentPt,nodeTxt) 72 plotNode(firstStr,cntrPt,parentPt,decisionNode) 73 secondDict = myTree[firstStr] 74 plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD 75 for key in secondDict: 76 if type(secondDict[key]).__name__ == 'dict': 77 plotTree(secondDict[key],cntrPt,str(key)) 78 else: 79 plotTree.xOff = plotTree.xOff + 1.0/plotTree.totalW 80 plotNode(secondDict[key],(plotTree.xOff,plotTree.yOff),\ 81 cntrPt,leafNode) 82 plotMidText((plotTree.xOff,plotTree.yOff),cntrPt,str(key)) 83 plotTree.yOff = plotTree.yOff + 1.0/plotTree.totalD 84 85 if __name__ == '__main__': 86 myTree = retrieveTree(2) 87 createPlot(myTree) 运行结果：
