机器学习——构建决策树

第1关：返回分类次数最多的分类名称

python 复制代码

import operator

def majorityCnt(classList):
    classCount = {}
    for i in classList:
        if i not in classCount:
            classCount[i] = 0
        classCount[i] += 1

    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
    return sortedClassCount[0][0]

第2关：创建树函数

python 复制代码

from ex03_lib import majorityCnt,splitDataSet,chooseBestFeatureToSplit

def createTree(dataSet,labels):
    classList = [example[-1] for example in dataSet]  #获取数据集的所有类别
    #### 请补充完整代码 ####
    if classList.count(classList[0]) == len(classList):
        return classList[0]
    if len(dataSet[0]) == 1:
        return majorityCnt(classList)
    bestFeat = chooseBestFeatureToSplit(dataSet)
    bestFeatLabel = labels[bestFeat]
    myTree = {bestFeatLabel:{}}
    del(labels[bestFeat])
    featValues = [example[bestFeat] for example in dataSet]
    uniqueVals = set(featValues)
    for value in uniqueVals:
        subLabels = labels[:]
        myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value), subLabels)
    #######################
    return myTree

第3关：获取叶子节点数目

python 复制代码

def getNumLeafs(myTree):
    numLeafs = 0
    if type(myTree).__name__ == 'dict':
        fi = list(myTree.keys())[0]
        se = myTree[fi]
        for i in se.keys():
            if type(se[i]).__name__ == 'dict':
                numLeafs += getNumLeafs(se[i])
            else:
                numLeafs += 1
    else:
        numLeafs += 1
    return numLeafs

第4关：获取树的层数

python 复制代码

def getTreeDepth(myTree):
    maxDepth = 0
    #### 请补充完整代码 ####
    fi = list(myTree.keys())[0]
    se = myTree[fi]
    for i in se.keys():
        if type(se[i]).__name__ == 'dict':
            thisDepth = 1 + getTreeDepth(se[i])
        else:
            thisDepth = 1
        if thisDepth > maxDepth: maxDepth = thisDepth
    #######################
    return maxDepth

第5关：注解树节点

python 复制代码

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
#定义决策树决策结果的特征，以字典的形式定义  
#下面的字典定义也可写作 decisionNode={boxstyle:'sawtooth',fc:'0.8'}  
#boxstyle为文本框的类型，sawtooth是锯齿形，fc是边框线粗细  
decisionNode = dict(boxstyle="sawtooth", fc="0.8")
leafNode = dict(boxstyle="round4", fc="0.8")
arrow_args = dict(arrowstyle="<-")
def plotNode(nodeTxt, centerPt, parentPt, nodeType):
    #annotate是关于一个数据点的文本  
    #nodeTxt为要显示的文本，centerPt为文本的中心点，parentPt为指向文本的点 
    #### 请补充完整代码 ####
    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') # 定义一个画布，背景为白色
    fig.clf() # 把画布清空
    #createPlot.ax1为全局变量，绘制图像的句柄，subplot为定义了一个绘图，
    #111表示figure中的图有1行1列，即1个，最后的1代表第一个图 
    #frameon表示是否绘制坐标轴矩形 
    #### 请补充完整代码 ####
    createPlot.ax1 = plt.subplot(111,frameon=False) 
    plotNode('a decision node',(0.2,0.2),(0.6,0.8),decisionNode) 
    plotNode('a leaf node',(0.6,0.1),(0.8,0.8),leafNode) 
    plt.show()
    #######################

第6关：绘制树形图

python 复制代码

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from ex03_lib import plotNode,getNumLeafs,getTreeDepth

def plotTree(myTree, parentPt, nodeTxt):
    numLeafs = getNumLeafs(myTree)                      #当前树的叶子数
    depth = getTreeDepth(myTree)                         #没有用到这个变量
    firstSides = list(myTree.keys())
    firstStr = firstSides[0]
    #cntrPt是文本中心点，parentPt指向文本中心点 
    #### 请补充完整代码 ####
    cntrPt = (plotTree.xOff + (1.0 + float(numLeafs))/2.0/plotTree.totalW, plotTree.yOff)
    plotMidText(cntrPt, parentPt, nodeTxt)                 #画分支上的键
    plotNode(firstStr, cntrPt, parentPt, decisionNode)
    secondDict = myTree[firstStr]
    plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD      #从上往下画
    for key in secondDict.keys():
    #如果是字典则是一个判断（内部）结点
        if type(secondDict[key]).__name__=='dict': 
            plotTree(secondDict[key],cntrPt,str(key))       
        else:   #打印叶子结点
            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
    #######################

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 createPlot(inTree):
    fig = plt.figure(1, facecolor='white')
    fig.clf()
    #### 请补充完整代码 ####
    axprops = dict(xticks=[], yticks=[])                   #定义横纵坐标轴  
    createPlot.ax1 = plt.subplot(111, frameon=False) 
    plotTree.totalW = float(getNumLeafs(inTree))       #全局变量宽度 = 叶子数
    plotTree.totalD = float(getTreeDepth(inTree))      #全局变量高度 = 深度
    #图形的大小是0-1 ，0-1
    plotTree.xOff = -0.5/plotTree.totalW;  
    #例如绘制3个叶子结点，坐标应为1/3,2/3,3/3
    #但这样会使整个图形偏右因此初始的，将x值向左移一点。
    plotTree.yOff = 1.0;
    plotTree(inTree, (0.5,1.0), '')
    #######################
    plt.show()