最近在学习Peter Harrington的《机器学习实战》，代码与书中的略有不同，但可以顺利运行。
from math import logimport operator# 计算熵def calcShannonEnt(dataset):num = len(dataset)labelCounts = {}for featVec in dataset:currentLabel = featVec[-1]if currentLabel not in labelCounts.keys():labelCounts[currentLabel] = 0labelCounts[currentLabel] += 1shannonEnt = 0for key in labelCounts:prob = float(labelCounts[key]/num)shannonEnt -= prob*log(prob, 2)return shannonEnt# 创建测试数据集def createDataset():dataset = [[1, 1, 'yes'],[1, 1, 'yes'],[1, 0, 'no'],[0, 1, 'no'],[0, 1, 'no']]# labels是特征的名称labels = ['no surfacing', 'flippers']return dataset, labels# 测试# mydata,labels = createDataset()# print(mydata)# print(calcShannonEnt(mydata))# 修改第一个实例的分类结果为maybe# mydata[0][-1] = 'maybe'# print(mydata)# print(calcShannonEnt(mydata))# 划分数据集def splitDataset(dataset, axis, value):retDataset = []for featVec in dataset:if featVec[axis] == value:reducedFeatVec = featVec[:axis]reducedFeatVec.extend(featVec[axis+1:])retDataset.append(reducedFeatVec)return retDataset# mydata,labels = createDataset()# print(mydata)# a = splitDataset(mydata, 0, 0)# b = splitDataset(mydata, 0, 1)# c = splitDataset(mydata, 1, 0)# d = splitDataset(mydata, 1, 1)# print(a)# print(b)# print(c)# print(d)# 选择最好的数据集划分方式def chooseBestFeatureToSplit(dataset):numFeatures = len(dataset[0]) - 1baseEntropy = calcShannonEnt(dataset)bestInfoGain = 0.0bestFeature = -1# 每个特征计算一次信息增益for i in range(numFeatures):featList = [example[i] for example in dataset]uniqueVals = set(featList)newEntropy = 0# 根据特征i的取值范围若干组（/子集/分支），计算每个子集的信息熵# 累计求和即可得到以特征i分组的数据集（包含所有数据）的新熵值# 新熵值小于原始数据集的熵值，因为数据更“有序”for value in uniqueVals:subDataset = splitDataset(dataset, i, value)prob = len(subDataset)/float(len(dataset))newEntropy += prob * calcShannonEnt(subDataset)infoGain = baseEntropy - newEntropyif infoGain > bestInfoGain:bestInfoGain = infoGainbestFeature = ireturn bestFeature# mydata,labels = createDataset()# bestFeature = chooseBestFeatureToSplit(mydata)# print(mydata)# print(bestFeature)# 多数表决# 处理了所有的特征后，类标签依然不是唯一的# 使用多数表决来决定叶子节点的分类def majorityCount(classList):classCount = {}for vote in classList:if vote not in classCount.keys():classCount[vote] = 0classCount[vote] += 1sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)return sortedClassCount[0][0]# 决策树def createTree(dataset, labels):classList = [example[-1] for example in dataset]# 如果数据集只有一个分类，返回分类结果（类标签）if classList.count(classList[0]) == len(classList):return classList[0]# 数据集只有分类结果，没有特征，根据多数表决返回分类结果（类标签）if len(dataset[0]) == 1:return majorityCount(classList)# 寻找划分数据集的最佳特征bestFeat = chooseBestFeatureToSplit(dataset)bestFeatLabel = labels[bestFeat]myTree = {bestFeatLabel: {}}del(labels[bestFeat])# 划分数据集，并在划分的每个子集中创建分支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# mydata, labels = createDataset()# mytree = createTree(mydata, labels)# print(mydata)# print(mytree)# [[1, 1, 'yes'], [1, 1, 'yes'], [1, 0, 'no'], [0, 1, 'no'], [0, 1, 'no']]# {'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}}# 使用决策树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# mydata, labels = createDataset()# mytree = createTree(mydata, labels)# print(classify(mytree, labels, [1, 1]))# 程序报错ValueError: 'no surfacing' is not in list# 因为createTree()函数中删除了最佳划分特征的标签 del(labels[bestFeat])# mydata, labels = createDataset()# mytree = createTree(mydata, labels)# mydata, labels = createDataset()# print(classify(mytree, labels, [1, 1]))# 储存决策树# pickle模块可以将小规模的数据存储在文件中，并在需要时读取出来def storeTree(inputTree, filename):import picklefw = open(filename, 'wb+')pickle.dump(inputTree, fw)fw.close()def grabTree(filename):import picklefr = open(filename, 'rb')return pickle.load(fr)# mytree = {'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}}# storeTree(mytree, 'C:/Users/Administrator/Desktop/classifier.txt')# a = grabTree('C:/Users/Administrator/Desktop/classifier.txt')# print(a)# 实例：使用决策树预测隐形眼镜类型f = open('D:/机器学习实战/machinelearninginaction/Ch03/lenses.txt')lenses = [line.strip().split('\t') for line in f.readlines()]lensesLabels = ['age', 'prescript', 'astigmatic', 'tearRate']lensesTree = createTree(lenses, lensesLabels)print(lenses)print(lensesLabels)print(lensesTree)# 绘制决策树from 机器学习实战 import treePlottertreePlotter.createPlot(lensesTree)
