机器学习实战—决策树（二）

#-*-coding:utf-8-*-
import ch
ch.set_ch()
import matplotlib.pyplot as plt

decisionNode = dict(boxstyle = "sawtooth",fc="0.8")
leafNode = dict(boxstyle="round4",fc = "0.8")
arrow_args = dict(arrowstyle = "<-")

#建立标注annotate
def plotNode(nodeTxt,centerPt,parentPt,nodeType):
# 标注内容   标注位置                                    标签位置
createPlot.ax1.annotate(nodeTxt,xy=parentPt,xycoords = 'axes fraction',xytext = centerPt,textcoords='axes fraction',va="center",\
#标签的格式         箭头的格式
ha = "center",bbox = nodeType,arrowprops=dict(arrowstyle="<-"))

def createPlotTemp():
#图名，可以是数字  背景颜色
fig = plt.figure("xihuan",facecolor = 'white')
fig.clf()#clear the figure
createPlotTemp.ax1 = plt.subplot(111,frameon = False)#产生一个子图，不显示坐标轴，但有坐标
plotNode(U'决策节点',(0.5,0.1),(0.1,0.5),decisionNode)
plotNode(U'叶节点',  (0.8,0.1),(0.3,0.8),leafNode)
plt.show()

#计算决策树的叶子节点的数目
def getNumLeafs(myTree):
numLeafs = 0
firstStr = myTree.keys()[0]
secondDict = myTree[firstStr]
for key in secondDict.keys():
if type(secondDict[key])==dict:
numLeafs += getNumLeafs(secondDict[key])
else: numLeafs += 1
return numLeafs
#计算树的深度
def getTreeDepth(myTree):
maxDepth = 0
firstStr = myTree.keys()[0]
secondDict = myTree[firstStr]
for key in secondDict.keys():
if type(secondDict[key])==dict:
thisDepth = 1 + getTreeDepth(secondDict[key])
else: thisDepth = 1;
if thisDepth > maxDepth:maxDepth = thisDepth
return maxDepth
#生成一棵决策树
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]

def plotMidText(centrPt,parentPt,txtString):
xMid = (parentPt[0]-centrPt[0])/2.0+centrPt[0]
yMid = (parentPt[1]-centrPt[1])/2.0+centrPt[1]
createPlot.ax1.text(xMid,yMid,txtString)

def createPlot(inTree):
fig = plt.figure("xihuan",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;#为了保证根结点标注于标签位置一致
plotTree(inTree,(0.5,1.0),'')
plt.show()

def plotTree(myTree,parentPt,nodeText):
numleafs = getNumLeafs(myTree)
depth = getTreeDepth(myTree)
firstStr = myTree.keys()[0] #第一个分类特征
centrPt = (plotTree.xoff + (1.0+float(numleafs))/2.0/plotTree.totalW,\
plotTree.yoff)
plotMidText(centrPt,parentPt,nodeText)#显示文本标签信息,根节点为空
plotNode(firstStr,centrPt,parentPt,decisionNode)#打印标注特征信息
secondDict = myTree[firstStr]
plotTree.yoff = plotTree.yoff-1.0/plotTree.totalD#调整下一个子数的Y方向位置
for key in secondDict.keys():
if type(secondDict[key])==dict:
plotTree(secondDict[key],centrPt,str(key))
else:#画出结点即可
plotTree.xoff = plotTree.xoff + 1.0/plotTree.totalW
plotNode(secondDict[key],(plotTree.xoff,plotTree.yoff),centrPt,leafNode)
plotMidText((plotTree.xoff,plotTree.yoff),centrPt,str(key))
plotTree.yoff = plotTree.yoff+1.0/plotTree.totalD#由于递归返回上一层，所以这里返回上层的y分量高度

mytree = retrieveTree(1)

#print getTreeDepth(mytree)
createPlot(mytree)