最近在学习Peter Harrington的《机器学习实战》，代码与书中的略有不同，但可以顺利运行。
import matplotlib.pyplot as plt# 定义文本框和箭头格式decisionNode = dict(boxstyle='sawtooth', fc='0.8')leafNode = dict(boxstyle='round4', fc='0.8')arrow_args = dict(arrowstyle='<-')# 使用文本注解绘制树节点def plotNode(nodeTxt, cencerPt, parentPt, nodeType):createPlot.axl.annotate(nodeTxt, xy=parentPt, xycoords='axes fraction',xytext=cencerPt, textcoords='axes fraction',va='center', ha='center', bbox=nodeType, arrowprops=arrow_args)# 第一版的createPlot()，之后还需要修改# def createPlot():#fig = plt.figure(1, facecolor='white')#fig.clf()#createPlot.ax1 = plt.subplot(111, frameon=False)#plotNode('a decision node', (0.5, 0.1), (0.1, 0.5), decisionNode)#plotNode('a leaf node', (0.8, 0.1), (0.3, 0.8), leafNode)#plt.show()# createPlot()# 报错'function' object has no attribute 'axl'，原因未知# 获取叶节点的数目def getNumLeafs(mytree):numLeafs = 0firstStr = list(mytree.keys())[0]secondDict = mytree[firstStr]for key in secondDict.keys():if type(secondDict[key]).__name__=='dict':numLeafs += getNumLeafs(secondDict[key])else:numLeafs += 1return numLeafs# mytree = {'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}}# print(getNumLeafs(mytree))# 获取决策树的层数def getTreeDepth(mytree):maxDepth = 0firstStr = list(mytree.keys())[0]secondDict = mytree[firstStr]for key in secondDict.keys():if type(secondDict[key]).__name__ == 'dict':thisDepth = 1 + getTreeDepth(secondDict[key])else:thisDepth = 1if thisDepth > maxDepth:maxDepth = thisDepthreturn maxDepth# mytree = {'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}}# print(getTreeDepth(mytree))# 在父子节点之间填充文本信息def plotMidText(centerPt, parentPt, txtString):xMid = (parentPt[0] - centerPt[0]) / 2.0 + centerPt[0]yMid = (parentPt[1] - centerPt[1]) / 2.0 + centerPt[1]createPlot.axl.text(xMid, yMid, txtString)def plotTree(mytree, parentPt, nodeTxt):numLeafs = getNumLeafs(mytree)depth = getTreeDepth(mytree)firstSides = list(mytree.keys())firstStr = firstSides[0] # 划分数据集的特征centerPt = (plotTree.xOff + (1.0 + float(numLeafs))/2.0/plotTree.totalW, plotTree.yOff) # x轴右移，(1+叶节点数目)/(2*决策树宽度)，y轴不变plotMidText(centerPt, parentPt, nodeTxt) # 绘制父子节点之间的文本标签（对于划分数据集的特征，其实没有文本标签）plotNode(firstStr, centerPt, parentPt, decisionNode) # 绘制父节点与指向子节点的箭头secondDict = mytree[firstStr] # 划分数据集特征的取值plotTree.yOff = plotTree.yOff - 1.0 / plotTree.totalD # y轴下移，1/决策树深度for key in secondDict.keys():# 如果分支下还有子树，调用函数本身，递归完成该分支的绘制if type(secondDict[key]).__name__ == 'dict':plotTree(secondDict[key], centerPt, str(key))# 如果分支下没有子树，仅一种分类结果，绘制叶子节点，添加父子节点之间的文本标签else:plotTree.xOff = plotTree.xOff + 1.0 / plotTree.totalW # x轴右移，1/(2*决策树宽度)plotNode(secondDict[key], (plotTree.xOff, plotTree.yOff), centerPt, leafNode) # 绘制叶子节点plotMidText((plotTree.xOff, plotTree.yOff), centerPt, str(key)) # 添加父子节点之间的文本标签plotTree.yOff = plotTree.yOff + 1.0/plotTree.totalD # y轴上移，恢复初始值，确保另外一个分支父节点的位置正确def createPlot(intree):fig = plt.figure(1, facecolor='white') # 创建一个新图形，前景色为白色fig.clf() # 清空绘图区axprops = dict(xticks=[], yticks=[]) # 定义坐标轴（空白）createPlot.axl = plt.subplot(111, frameon=False, **axprops) # 创建子图，无边框，无坐标值plotTree.totalW = float(getNumLeafs(intree)) # 设置全局变量，记录决策树的宽带plotTree.totalD = float(getTreeDepth(intree)) # 设置全局变量，记录决策树的深度plotTree.xOff = -0.5 / plotTree.totalW # 设置全局变量，追踪节点的x轴坐标plotTree.yOff = 1.0 # 设置全局变量，追踪节点的y轴坐标plotTree(intree, (0.5, 1.0), '')plt.show()# mytree = {'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}}# createPlot(mytree)# mytree = {'no surfacing': {1: {'flippers': {0: 'no', 1: 'yes'}}, 0: 'no'}}# createPlot(mytree)import matplotlib.pyplot as plt# 定义文本框和箭头格式decisionNode = dict(boxstyle='sawtooth', fc='0.8')leafNode = dict(boxstyle='round4', fc='0.8')arrow_args = dict(arrowstyle='<-')# 使用文本注解绘制树节点def plotNode(nodeTxt, cencerPt, parentPt, nodeType):createPlot.axl.annotate(nodeTxt, xy=parentPt, xycoords='axes fraction',xytext=cencerPt, textcoords='axes fraction',va='center', ha='center', bbox=nodeType, arrowprops=arrow_args)# 第一版的createPlot()，之后还需要修改# def createPlot():#fig = plt.figure(1, facecolor='white')#fig.clf()#createPlot.ax1 = plt.subplot(111, frameon=False)#plotNode('a decision node', (0.5, 0.1), (0.1, 0.5), decisionNode)#plotNode('a leaf node', (0.8, 0.1), (0.3, 0.8), leafNode)#plt.show()# createPlot()# 报错'function' object has no attribute 'axl'，原因未知# 获取叶节点的数目def getNumLeafs(mytree):numLeafs = 0firstStr = list(mytree.keys())[0]secondDict = mytree[firstStr]for key in secondDict.keys():if type(secondDict[key]).__name__=='dict':numLeafs += getNumLeafs(secondDict[key])else:numLeafs += 1return numLeafs# mytree = {'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}}# print(getNumLeafs(mytree))# 获取决策树的层数def getTreeDepth(mytree):maxDepth = 0firstStr = list(mytree.keys())[0]secondDict = mytree[firstStr]for key in secondDict.keys():if type(secondDict[key]).__name__ == 'dict':thisDepth = 1 + getTreeDepth(secondDict[key])else:thisDepth = 1if thisDepth > maxDepth:maxDepth = thisDepthreturn maxDepth# mytree = {'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}}# print(getTreeDepth(mytree))# 在父子节点之间填充文本信息def plotMidText(centerPt, parentPt, txtString):xMid = (parentPt[0] - centerPt[0]) / 2.0 + centerPt[0]yMid = (parentPt[1] - centerPt[1]) / 2.0 + centerPt[1]createPlot.axl.text(xMid, yMid, txtString)def plotTree(mytree, parentPt, nodeTxt):numLeafs = getNumLeafs(mytree)depth = getTreeDepth(mytree)firstSides = list(mytree.keys())firstStr = firstSides[0] # 划分数据集的特征centerPt = (plotTree.xOff + (1.0 + float(numLeafs))/2.0/plotTree.totalW, plotTree.yOff) # x轴右移，(1+叶节点数目)/(2*决策树宽度)，y轴不变plotMidText(centerPt, parentPt, nodeTxt) # 绘制父子节点之间的文本标签（对于划分数据集的特征，其实没有文本标签）plotNode(firstStr, centerPt, parentPt, decisionNode) # 绘制父节点与指向子节点的箭头secondDict = mytree[firstStr] # 划分数据集特征的取值plotTree.yOff = plotTree.yOff - 1.0 / plotTree.totalD # y轴下移，1/决策树深度for key in secondDict.keys():# 如果分支下还有子树，调用函数本身，递归完成该分支的绘制if type(secondDict[key]).__name__ == 'dict':plotTree(secondDict[key], centerPt, str(key))# 如果分支下没有子树，仅一种分类结果，绘制叶子节点，添加父子节点之间的文本标签else:plotTree.xOff = plotTree.xOff + 1.0 / plotTree.totalW # x轴右移，1/(2*决策树宽度)plotNode(secondDict[key], (plotTree.xOff, plotTree.yOff), centerPt, leafNode) # 绘制叶子节点plotMidText((plotTree.xOff, plotTree.yOff), centerPt, str(key)) # 添加父子节点之间的文本标签plotTree.yOff = plotTree.yOff + 1.0/plotTree.totalD # y轴上移，恢复初始值，确保另外一个分支父节点的位置正确def createPlot(intree):fig = plt.figure(1, facecolor='white') # 创建一个新图形，前景色为白色fig.clf() # 清空绘图区axprops = dict(xticks=[], yticks=[]) # 定义坐标轴（空白）createPlot.axl = plt.subplot(111, frameon=False, **axprops) # 创建子图，无边框，无坐标值plotTree.totalW = float(getNumLeafs(intree)) # 设置全局变量，记录决策树的宽带plotTree.totalD = float(getTreeDepth(intree)) # 设置全局变量，记录决策树的深度plotTree.xOff = -0.5 / plotTree.totalW # 设置全局变量，追踪节点的x轴坐标plotTree.yOff = 1.0 # 设置全局变量，追踪节点的y轴坐标plotTree(intree, (0.5, 1.0), '')plt.show()# mytree = {'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}}# createPlot(mytree)# mytree = {'no surfacing': {1: {'flippers': {0: 'no', 1: 'yes'}}, 0: 'no'}}# createPlot(mytree)
