決策樹框架：

 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 def plotNode(nodeTxt, centerPt, parentPt, nodeType):
10     createPlot.ax1.annotate(nodeTxt, xy=parentPt, xycoords=‘
 
axes fraction‘, 11                             xytext=centerPt, textcoords=‘axes fraction‘, 12                             va=‘center‘, ha=‘center‘, bbox=nodeType, arrowprops 13                                 =arrow_args)
14 
15 
16 def getNumLeafs(myTree):
17     numLeafs = 0
18     firstStr = list(myTree.keys())[0]
 
19     secondDict = myTree[firstStr]
20     for key in secondDict:
21         if (type(secondDict[key]).__name__ == ‘dict‘):
22             numLeafs += getNumLeafs(secondDict[key])
23         else:
24             numLeafs += 1
25     return numLeafs
26 
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 
41 def retrieveTree(i):
42     # 預先設置樹的信息
43     listOfTree = []
44     return listOfTree[i]
45 
46 
47 def createPlot(inTree):
48     fig = plt.figure(1, facecolor=‘white‘)
49     fig.clf()
50     axprops = dict(xticks=[], yticks=[])
51     createPlot.ax1 = plt.subplot(111, frameon=False, **axprops)
52     plotTree.totalW = float(getNumLeafs(inTree))
53     plotTree.totalD = float(getTreeDepth(inTree))
54     plotTree.xOff = -0.5 / plotTree.totalW;
55     plotTree.yOff = 1.0
56     plotTree(inTree, (0.5, 1.0), ‘‘)
57     plt.title(‘kaifeng.58.com\n‘)
58     plt.show()
59 
60 
61 def plotMidText(cntrPt, parentPt, txtString):
62     xMid = (parentPt[0] - cntrPt[0]) / 2.0 + cntrPt[0]
63     yMid = (parentPt[1] - cntrPt[1]) / 2.0 + cntrPt[1]
64     createPlot.ax1.text(xMid, yMid, txtString)
65 
66 
67 def plotTree(myTree, parentPt, nodeTxt):
68     numLeafs = getNumLeafs(myTree)
69     depth = getTreeDepth(myTree)
70     firstStr = list(myTree.keys())[0]
71     cntrPt = (plotTree.xOff + (1.0 + float(numLeafs)) / 2.0 / plotTree.totalW, 72               plotTree.yOff)
73     plotMidText(cntrPt, parentPt, nodeTxt)
74     plotNode(firstStr, cntrPt, parentPt, decisionNode)
75     secondDict = myTree[firstStr]
76     plotTree.yOff = plotTree.yOff - 1.0 / plotTree.totalD
77     for key in secondDict:
78         if type(secondDict[key]).__name__ == ‘dict‘:
79             plotTree(secondDict[key], cntrPt, str(key))
80         else:
81             plotTree.xOff = plotTree.xOff + 1.0 / plotTree.totalW
82             plotNode(secondDict[key], (plotTree.xOff, plotTree.yOff), 83                      cntrPt, leafNode)
84             plotMidText((plotTree.xOff, plotTree.yOff), cntrPt, str(key))
85     plotTree.yOff = plotTree.yOff + 1.0 / plotTree.totalD
86 
87 
88 if __name__ == ‘__main__‘:
89     myTree = retrieveTree(2)
90     createPlot(myTree)

構造信息：

1  [{‘no surfacing‘: {0: ‘no‘, 1: {‘flipper‘: {0: ‘no‘, 1: ‘yes‘}}}},
2                   {‘no surfacing‘: {0: ‘no‘, 1: {‘flipper‘: {0: {‘head‘: {0: ‘no‘, 1: ‘yes‘}}, 1: ‘no‘}}}},
3                   {‘House prices <= 2000‘: {
4                       0: {‘Room size >= 50‘: {0: ‘Yes‘, 1: ‘No‘}}, 1: ‘No‘}}]

結果：

Python爬蟲(三)——開封市58同城出租房決策樹構建