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网上的版本好像好久都没更新了treePlotter是没有人用了么。今天学习的时候发现有些地方已经改了，我改的是在python 3.6 上的运行版本，需要导入matplotlib.pyplot

import matplotlib.pyplot as plt# 定义决策树决策结果属性descisionNode = dict(box, fc="0.8")leafNode = dict(box, fc="0.8")arrow_args = dict(arrow)
# myTree = {"no surfacing": {0: "no", 1: {"flippers": {0: "no", 1: "yes"}}}}def plotNode(nodeTxt, centerPt, parentPt, nodeType):    # nodeTxt为要显示的文本，centerNode为文本中心点, nodeType为箭头所在的点， parentPt为指向文本的点    createPlot.ax1.annotate(nodeTxt, xy=parentPt, xycoords="axes fraction",                             xytext=centerPt, textcoords="axes fraction",                              va="center", ha="center", bbox=nodeType, arrowprops=arrow_args)# def createPlot():#     fig = plt.figure(1, facecolor="white")#     fig.clf()#     # createPlot.ax1为全局变量,绘制图像句柄#     # frameon表示是否绘制坐标轴矩形#     createPlot.ax1 = plt.subplot(111, frameon=False)#     plotNode("a decision node", (0.5, 0.1), (0.1, 0.5), descisionNode)#     plotNode("a leaf node", (0.8, 0.1), (0.3, 0.8), leafNode)#     plt.show()# 这个是用来测试的# -----------分割线-------------# 获取树的叶子数和树的深度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 numLeafsdef getTreeDepth(myTree):    maxDepth = 0    firstStr = list(myTree.keys())[0]     # 这个是改的地方，原来myTree.keys()返回的是dict_keys类，不是列表，运行会报错。有好几个地方这样    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# ---------分割线-------------# 制图def createPlot(inTree):    fig = plt.figure(1, facecolor="white")    fig.clf()    axprops = {"xticks": None, "yticks": None}    createPlot.ax1 = plt.subplot(111, frameon=False)    plotTree.totalW = float(getNumLeafs(inTree))     # 全局变量宽度 = 叶子数目    plotTree.totalD = float(getTreeDepth(inTree))     # 全局变量高度 = 深度    plotTree.xOff = -0.5/plotTree.totalW    plotTree.yOff = 1.0    plotTree(inTree, (0.5, 1.0), "")    plt.show()def plotTree(myTree, parentPt, nodeTxt):    numLeafs = getNumLeafs(myTree)    depth = getTreeDepth(myTree)    firstStr = list(myTree.keys())[0]    # cntrPt文本中心点， parentPt指向文本中心的点    cntrPt = (plotTree.xOff + (1.0 + float(numLeafs))/2.0/plotTree.totalW, plotTree.yOff)    plotMidText(cntrPt, parentPt, nodeTxt)    plotNode(firstStr, cntrPt, parentPt, descisionNode)    seconDict = myTree[firstStr]    plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD    for key in seconDict.keys():        if type(seconDict[key]).__name__ == "dict":            plotTree(seconDict[key], cntrPt, str(key))        else:            plotTree.xOff = plotTree.xOff + 1.0/plotTree.totalW            plotNode(seconDict[key], (plotTree.xOff, plotTree.yOff), cntrPt, leafNode)            plotMidText((plotTree.xOff, plotTree.yOff), cntrPt, str(key))    plotTree.yOff = plotTree.yOff + 1.0/plotTree.totalDdef 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)# createPlot(myTree)

　　这个treePlotter导入了就可以把原来得到的决策树模型导入啦，而且要注意是以字典形式导入，所以保存和导入文件的时候最好用json。

发布5分钟之后，突然发现已经有人改过了，那就只算是个学习笔记吧 -  -

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