决策树预测学生成绩

前言

决策树主要用于分类问题。导入学生成绩，用0-1预测学生是否通过考试；
主要是放下 treePlotter 模块的代码；

一、决策树预测代码

之前的怎么又失效了。。。提取码: o54j

二、treePlotter模块

自定义treePloter模块，命名为treePloter,新建Python文件__init__,复制代码，
放在父文件夹下：

i# _*_ coding: UTF-8 _*_

import matplotlib.pyplot as plt


"""绘决策树的函数"""
decisionNode = dict(boxstyle="sawtooth", fc="0.8")  # 定义分支点的样式
leafNode = dict(boxstyle="round4", fc="0.8")  # 定义叶节点的样式
arrow_args = dict(arrowstyle="<-")  # 定义箭头标识样式


# 计算树的叶子节点数量
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


# 计算树的最大深度
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


# 画出节点
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)


# 标箭头上的文字
def plotMidText(cntrPt, parentPt, txtString):
    lens = len(txtString)
    xMid = (parentPt[0] + cntrPt[0]) / 2.0 - lens * 0.002
    yMid = (parentPt[1] + cntrPt[1]) / 2.0
    createPlot.ax1.text(xMid, yMid, txtString)


def plotTree(myTree, parentPt, nodeTxt):
    numLeafs = getNumLeafs(myTree)
    depth = getTreeDepth(myTree)
    firstStr = list(myTree.keys())[0]
    cntrPt = (plotTree.x0ff + \
              (1.0 + float(numLeafs)) / 2.0 / plotTree.totalW, plotTree.y0ff)
    plotMidText(cntrPt, parentPt, nodeTxt)
    plotNode(firstStr, cntrPt, parentPt, decisionNode)
    secondDict = myTree[firstStr]
    plotTree.y0ff = plotTree.y0ff - 1.0 / plotTree.totalD
    for key in secondDict.keys():
        if type(secondDict[key]).__name__ == 'dict':
            plotTree(secondDict[key], cntrPt, str(key))
        else:
            plotTree.x0ff = plotTree.x0ff + 1.0 / plotTree.totalW
            plotNode(secondDict[key], \
                     (plotTree.x0ff, plotTree.y0ff), cntrPt, leafNode)
            plotMidText((plotTree.x0ff, plotTree.y0ff) \
                        , cntrPt, str(key))
    plotTree.y0ff = plotTree.y0ff + 1.0 / plotTree.totalD


def createPlot(inTree):
    fig = plt.figure(1, 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.x0ff = -0.5 / plotTree.totalW
    plotTree.y0ff = 1.0
    plotTree(inTree, (0.5, 1.0), '')
    plt.show()

if __name__== '__main__':
    createPlot()