关于决策树可视化的treePlotter(学习笔记）

Posted 2020-10-27 moler1995

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(boxstyle=‘sawtooth‘, fc=‘0.8‘)
leafNode = dict(boxstyle=‘round4‘, fc=‘0.8‘)
arrow_args = dict(arrowstyle=‘<-‘)
# 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 numLeafs


def 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.totalD


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)

# 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 -  -