機器學習實戰——決策樹：matplotlib繪圖

書籍：《機器學習實戰》中文版
IDE：PyCharm Edu 4.02

環境：Adaconda3  python3.6

第一個例子：

import matplotlib.pyplot as plt

#定義文本框和箭頭格式
decisionNode = dict(boxstyle="sawtooth",fc="0.8")
leafNode = dict(boxstyle="round4",fc="0.8")
arrow_args = dict(arrowstyle="<-")   #使用反轉箭頭
# 繪製帶箭頭的註解
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)
# 主函數
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())

結果：

註釋：

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

第二個例子：

import matplotlib.pyplot as plt
# 得到葉子節點數目 以便確定x軸範圍
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
# 得到樹的深度 以便確定y軸範圍
# 即樹（字典）中字典類型的數目
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
# 預先存儲樹的信息
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]

#定義文本框和箭頭格式
decisionNode = dict(boxstyle="sawtooth",fc="0.8")
leafNode = dict(boxstyle="round4",fc="0.8")
arrow_args = dict(arrowstyle="<-")   #使用反轉箭頭
# 繪製帶箭頭的註解
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)
#樹的繪製
def plotTree(myTree,parentPt,nodeTxt):
    numLeafs = getNumLeafs(myTree)
    #depth = getTreeDepth(myTree)   沒有用到此變量
    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座標相同時，不繪製箭頭。
    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
# 主函數
def createPlot(inTree):
    fig = plt.figure()
    fig.clf()
    axprops = dict(xticks=[],yticks=[])   #表示不顯示座標軸
    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)))

結果：

小結—類型的判斷問題：

（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

比如上一篇中決策樹分類函數的定義

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