李宏毅機器學習 Machine_Learning_2019_Task9

利用 Python 結合 Matplotlib 繪製樹圖形

參考：https://blog.csdn.net/maotianyi941005/article/details/82349032

import matplotlib.pyplot as plt
decisionNode = dict(boxstyle="sawtooth", fc="0.8")
leafNode = dict(boxstyle="round4", fc="0.8")
arrow_args = dict(arrowstyle="<-")
def getNumLeafs(myTree):
    # 初始化結點數
    numLeafs=0
    firstSides = list(myTree.keys())
    # 找到輸入的第一個元素,第一個關鍵詞爲劃分數據集類別的標籤
    firstStr = firstSides[0]
    secondDict = myTree[firstStr]
    # 測試數據是否爲字典形式
    for key in secondDict.keys():
        # type判斷子結點是否爲字典類型
        if type(secondDict[key]).__name__=='dict':
            numLeafs+=getNumLeafs(secondDict[key])
            #若子節點也爲字典，則也是判斷結點，需要遞歸獲取num
        else:  numLeafs+=1
    # 返回整棵樹的結點數
    return numLeafs
def getTreeDepth(myTree):
    maxDepth = 0
    firstSides = list(myTree.keys())
    firstStr = firstSides[0]
    # 獲取劃分類別的標籤
    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
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 )
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, va="center", ha="center", rotation=30)
def plotTree(myTree, parentPt, nodeTxt):
    # 計算樹的寬度  totalW
    numLeafs = getNumLeafs(myTree)
    # 計算樹的高度 存儲在totalD
    depth = getTreeDepth(myTree)
    firstSides = list(myTree.keys())
    # firstStr = myTree.keys()[0] 續作修改
    # 找到輸入的第一個元素
    firstStr = firstSides[0] 
    # 按照葉子結點個數劃分x軸
    cntrPt = (plotTree.xOff + (1.0 + float(numLeafs))/2.0/plotTree.totalW, plotTree.yOff)
    # 標註子結點屬性值
    plotMidText(cntrPt, parentPt, nodeTxt) 
    plotNode(firstStr, cntrPt, parentPt, decisionNode)
    secondDict = myTree[firstStr]
    # y方向上的擺放位置，自上而下繪製，遞減y值
    plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD 
    for key in secondDict.keys():
        # 判斷是否爲字典，不是則爲葉子結點
        if type(secondDict[key]).__name__=='dict':
            # 遞歸查找
            plotTree(secondDict[key],cntrPt,str(key))
        # 到達葉子結點
        else:   
            # x方向計算結點座標
            plotTree.xOff = plotTree.xOff + 1.0/plotTree.totalW
            plotNode(secondDict[key], (plotTree.xOff, plotTree.yOff), cntrPt, leafNode)
            # 添加文本信息
            plotMidText((plotTree.xOff, plotTree.yOff), cntrPt, str(key))
    # 下次重新調用時恢復y
    plotTree.yOff = plotTree.yOff + 1.0/plotTree.totalD 

    # 主函數
def createPlot(inTree): 
    fig = plt.figure(1, facecolor='white')
    fig.clf()
    axprops = dict(xticks=[], yticks=[])
    # 在繪圖區上繪製兩個代表不同類型的樹節點
    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()
    
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]


data = getDataSet('E:/bigdata/watermelon_3a.csv')
label = data.loc[:,'label']
dataSet = data.loc[   :  , 'color':'sugar_ratio']
dataSet = dataSet.values.tolist()
dataSet
labels = label.values.tolist()
labels
mytree = dtm.createTree(dataSet,labels)
print(mytree)
createPlot(mytree)