【机器学习实战】决策树 python代码实现

---

typora-copy-images-to: upload

第三章 决策树

3.1决策树的构造

• 优点：计算复杂度不高 输出结果易于理解 对中间值的缺失不敏感，可以处理不相关的特征数据
• 缺点：可能会产生过度匹配的问题
• 适用数据类型 数据型和标称型

一般流程：

收集数据 准备数据 分析数据 训练算法 测试算法 使用算法

数据重新加载的问题

代码实现

# coding:UTF-8
from math import log


# 计算给定数据集的香农嫡
def calcShannonEnt(dataSet):
    numEntries = len(dataSet)
    labelCounts = {}
    for featVec in dataSet:
        currentLabel = featVec[-1]
        if currentLabel not in labelCounts.keys():
            labelCounts[currentLabel] = 0
        labelCounts[currentLabel] += 1
    shannonEnt = 0.0
    for key in labelCounts:
        prob = float(labelCounts[key]) / numEntries
        shannonEnt -= prob * log(prob, 2)
    return shannonEnt


def createDataSet():
    dataSet = [[1, 1, 'yes'], [1, 1, 'yes'], [0, 1, 'no'], [0, 1, 'no']]
    labels = ['no surfacing', 'flippers']
    return dataSet, labels


def main():
    myDat, labels = createDataSet()
    print(myDat)
    print("%f" % calcShannonEnt(myDat))


if __name__ == "__main__":
    main()

实现截图

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-Wktrh11D-1664967392793)(https://cdn.jsdelivr.net/gh/hudiework/img@main/image-20221004200407218.png)]

获取最大信息增益的方法获取数据集 实现代码

 myDat[0][-1] = 'maybe'
    print(myDat)
    print("-------------")
    print(calcShannonEnt(myDat))

实现截图

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-RgXGT47J-1664967392793)(https://cdn.jsdelivr.net/gh/hudiework/img@main/image-20221004200334343.png)]

3.2 划分数据集

3-2 按照给定特征划分数据集

# 选择最好的数据集划分方式
def chooseBestFeatureToSplit(dataSet):
    numFeatures = len(dataSet[0]) - 1
    baseEntropy = calcShannonEnt(dataSet)
    bestInfoGain = 0.0; bestFeature = -1
    for i in range(numFeatures):
        featList = [example[i] for example in dataSet]
        uniqueVals = set(featList)
        newEntropy = 0.0
        for value in uniqueVals:
            subDataSet = splitDataSet(dataSet, i, value)
            prob = len(subDataSet) / float(len(dataSet))
            newEntropy += prob * calcShannonEnt(subDataSet)
        infoGain = baseEntropy - newEntropy
        if (infoGain > bestInfoGain):
            bestInfoGain = infoGain
            bestFeature = i
    return bestFeature

3.1.3 递归构建决策树

创建树的代码：

def createTree(dataSet, labels):
    classList = [example[-1] for example in dataSet]
    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

运行代码

myDat, labels = createDataSet()
mytree = createTree(myDat,labels)
print(mytree)

实现截图

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-qyptJh00-1664967392793)(https://cdn.jsdelivr.net/gh/hudiework/img@main/image-20221004200245619.png)]

3.2.1 在python中使用Matplotlib注解绘制树形图

注解工具annotations

代码实现

from pylab import mpl
 
# 设置中文显示字体
mpl.rcParams["font.sans-serif"] = ["SimHei"]
# 设置正常显示符号
mpl.rcParams["axes.unicode_minus"] = False

def createPlot():
    fig = plt.figure(1, facecolor='white')
    fig.clf()
    createPlot.ax1 = plt.subplot(111, frameon=False)
    plotNode(U'Decision Node', (0.5, 0.1), (0.1, 0.5), decisionNode)
    plotNode(U'Leaf Node', (0.8, 0.1), (0.3, 0.8), leafNode)
    plt.show()

实现截图

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-l4B1iqLy-1664967392794)(https://cdn.jsdelivr.net/gh/hudiework/img@main/image-20221005132825703.png)]

3.2.2 构造注解树

程序清单3-6 获取叶节点的树目和树的层数

MAC pycharm打不开

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-MlxewiAV-1664967392794)(https://cdn.jsdelivr.net/gh/hudiework/img@main/image-20221005140639325.png)]

代码实现

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]
    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 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]

结果截图

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-poBP9cv7-1664967392794)(https://cdn.jsdelivr.net/gh/hudiework/img@main/image-20221005143942150.png)]

程序清单3-7 plotTree函数

程序代码

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)


def plotTree(myTree, parentPt, nodeTxt):
    numLeafs = getNumLeafs(myTree)
    depth = getTreeDepth(myTree)
    firstStr = list(myTree.keys())[0]
    cntrPt = (plotTree.xOff + (1.0 + float(numLeafs)) / 2.0 / plotTree.totalW, \
              plotTree.yOff)
    plotMidText(cntrPt, parentPt, nodeTxt)
    plotNode(firstStr, cntrPt, parentPt, decisionNode)
    secondDict = myTree[firstStr]
    plotTree.yOff = plotTree.yOff - 1.0 / plotTree.totalD
    for key in secondDict.keys():
        if type(secondDict[key]).__name__ == 'dict':
            plotTree(secondDict[key], cntrPt, str(key))
        else:
            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 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.xOff = - 0.5 / plotTree.totalW;
    plotTree.yOff = 1.0;
    plotTree(inTree, (0.5, 1.0), '')
    plt.show()

执行代码

matplotlib.use('TkAgg')
mytree = retrieveTree(0)
# print(getNumLeafs(mytree))
# print(getTreeDepth(mytree))
createPlot(mytree)

实现截图

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-ICreMC7z-1664967392794)(https://cdn.jsdelivr.net/gh/hudiework/img@main/image-20221005172516454.png)]

3.3 测试和存储分类器

3.3.1 测试算法：使用决策树进行分类 程序3-8 使用决策树的分类函数

def classify(inputTree, featLabels, testVec):
    firstStr = list(inputTree.keys())[0]
    secondDict = inputTree[firstStr]
    featIndex = featLabels.index(firstStr)
    for key in secondDict.keys():
        if testVec[featIndex] == key:
            if type(secondDict[key]).__name__ == 'dict':
                classLabel = classify(secondDict[key], featLabels, testVec)
            else:
                classLabel = secondDict[key]
    return classLabel

实现代码

matplotlib.use('TkAgg')
myDat, labels = createDataSet()

mytree = retrieveTree(0)
# print(getNumLeafs(mytree))
# print(getTreeDepth(mytree))
# createPlot(mytree)
print(classify(mytree, labels, [1, 0]))
print(classify(mytree, labels, [1, 1]))

实现截图

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-3118drTK-1664967392794)(https://cdn.jsdelivr.net/gh/hudiework/img@main/image-20221005173540215.png)]

3.3.2 使用算法：决策树的存储 3-9 使用pickle模块存储决策树

代码实现

def storeTree(inputTree, filename):
    import pickle
    fw = open(filename, 'wb')
    pickle.dump(inputTree, fw)
    fw.close()


def grabTree(filename):
    import pickle
    fr = open(filename, 'rb')
    return pickle.load(fr)

执行代码

mytree = retrieveTree(0)
print(mytree)
storeTree(mytree, 'classifierStorage.txt')
print(grabTree('classifierStorage.txt'))

实现截图

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-Fl8VDsSB-1664967392795)(https://cdn.jsdelivr.net/gh/hudiework/img@main/image-20221005180153803.png)]

使用决策树预测隐形眼镜类型

执行代码

matplotlib.use('TkAgg')
mytree = retrieveTree(0)
print(mytree)
storeTree(mytree, 'classifierStorage.txt')
# print(grabTree('classifierStorage.txt'))

fr = open('lenses.txt')
lenses = [inst.strip().split('\t') for inst in fr.readlines()]
# print("--------")
# print(lenses)
lensesLabels = ['age', 'prescipt', 'astigmatic', 'tearRate']
lensesTree = createTree(lenses, lensesLabels)
print(lensesTree)
createPlot(lensesTree)

实现截图

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-8BlGHhSG-1664967392795)(https://cdn.jsdelivr.net/gh/hudiework/img@main/image-20221005185537583.png)]
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-KOS7GKdd-1664967458351)(https://cdn.jsdelivr.net/gh/hudiework/img@main/image-20221005185537583.png)]