机器学习实战——决策树：matplotlib绘图

书籍：《机器学习实战》中文版
IDE：PyCharm Edu 4.02

环境：Adaconda3  python3.6

第一个例子：

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="<-")   #使用反转箭头
# 绘制带箭头的注解
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)
# 主函数
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()

print(createPlot())

结果：



注释：

关于matplotlib中的annotate：http://matplotlib.org/users/annotations_intro.html

第二个例子：

import matplotlib.pyplot as plt
# 得到叶子节点数目 以便确定x轴范围
def getNumLeafs(mytree):
numLeafs = 0
firstStr = list(mytree.keys())[0]
secondDict = mytree[firstStr]
for key in secondDict.keys():
if type(secondDict[key]).__name__=='dict':
numLeafs += getNumLeafs(secondDict[key])
else: numLeafs += 1
return numLeafs
# 得到树的深度 以便确定y轴范围
# 即树（字典）中字典类型的数目
def getTreeDepth(myTree):
maxDepth = 0
firstStr = 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 = 1
if thisDepth>maxDepth:
maxDepth = thisDepth
return maxDepth
# 预先存储树的信息
def retrieveTree(i):
listOfTrees =[{'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}},
{'no surfacing': {0: 'no', 1: {'flippers': {0: {'head': {0: 'no', 1: 'yes'}}, 1: 'no'}}}}
]
return listOfTrees[i]

#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="<-")   #使用反转箭头
# 绘制带箭头的注解
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)
#文本信息的填充
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)
#树的绘制
def plotTree(myTree,parentPt,nodeTxt):
numLeafs = getNumLeafs(myTree)
#depth = getTreeDepth(myTree)   没有用到此变量
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) #cntrPt,parentPt坐标相同时，不绘制箭头。
secondDict = myTree[firstStr]
plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD
for key in secondDict.keys():
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
# 主函数
def createPlot(inTree):
fig = plt.figure()
fig.clf()
axprops = dict(xticks=[],yticks=[])   #表示不显示坐标轴
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.show()
print(createPlot(retrieveTree(0)))

结果：



小结—类型的判断问题：

（1）使用type().__name__  

比如本文中的使用

for key in secondDict.keys():
if type(secondDict[key]).__name__=='dict':
numLeafs += getNumLeafs(secondDict[key])
else: numLeafs += 1

（2）使用isinstance

比如上一篇中决策树分类函数的定义

def classify(inputTree,featLabels,testVec):
firstStr = list(inputTree.keys())[0]
secondDict = inputTree[firstStr]
featIndex = featLabels.index(firstStr)
key = testVec[featIndex]
valueOfFeat = secondDict[key]
if isinstance(valueOfFeat,dict):
classLabel = classify(valueOfFeat,featLabels,testVec)
else: classLabel = valueOfFeat
return classLabel