Python爬虫(三)——开封市58同城出租房决策树构建

决策树框架：

 # coding=utf-8
 import matplotlib.pyplot as plt

 decisionNode = dict(boxstyle='sawtooth', fc='')
 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 = []
     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('kaifeng.58.com\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)

构造信息：

  [{'no surfacing': {0: 'no', 1: {'flipper': {0: 'no', 1: 'yes'}}}},
                   {'no surfacing': {0: 'no', 1: {'flipper': {0: {'head': {0: 'no', 1: 'yes'}}, 1: 'no'}}}},
                   {'House prices <= 2000': {
                       1: {'Room size >= 50': {1: 'Yes', 0: 'No'}}, 0: 'No'}}]

结果：