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- # -*- coding: UTF-8 -*-
- from matplotlib.font_manager import FontProperties
- import matplotlib.pyplot as plt
- from math import log
- import operator
- import pickle
-
- """
- 函数说明:计算给定数据集的经验熵(香农熵)
-
- Parameters:
- dataSet - 数据集
- Returns:
- shannonEnt - 经验熵(香农熵)
- Author:
- Jack Cui
- Blog:
- http://blog.csdn.net/c406495762
- Modify:
- 2017-07-24
- """
- def calcShannonEnt(dataSet):
- numEntires = len(dataSet) #返回数据集的行数
- labelCounts = {} #保存每个标签(Label)出现次数的字典
- for featVec in dataSet: #对每组特征向量进行统计
- currentLabel = featVec[-1] #提取标签(Label)信息
- if currentLabel not in labelCounts.keys(): #如果标签(Label)没有放入统计次数的字典,添加进去
- labelCounts[currentLabel] = 0
- labelCounts[currentLabel] += 1 #Label计数
- shannonEnt = 0.0 #经验熵(香农熵)
- for key in labelCounts: #计算香农熵
- prob = float(labelCounts[key]) / numEntires #选择该标签(Label)的概率
- shannonEnt -= prob * log(prob, 2) #利用公式计算
- return shannonEnt #返回经验熵(香农熵)
-
- """
- 函数说明:创建测试数据集
-
- Parameters:
- 无
- Returns:
- dataSet - 数据集
- labels - 特征标签
- Author:
- Jack Cui
- Blog:
- http://blog.csdn.net/c406495762
- Modify:
- 2017-07-20
- """
- def createDataSet():
- dataSet = [[0, 0, 0, 0, 'no'], #数据集
- [0, 0, 0, 1, 'no'],
- [0, 1, 0, 1, 'yes'],
- [0, 1, 1, 0, 'yes'],
- [0, 0, 0, 0, 'no'],
- [1, 0, 0, 0, 'no'],
- [1, 0, 0, 1, 'no'],
- [1, 1, 1, 1, 'yes'],
- [1, 0, 1, 2, 'yes'],
- [1, 0, 1, 2, 'yes'],
- [2, 0, 1, 2, 'yes'],
- [2, 0, 1, 1, 'yes'],
- [2, 1, 0, 1, 'yes'],
- [2, 1, 0, 2, 'yes'],
- [2, 0, 0, 0, 'no']]
- labels = ['年龄', '有工作', '有自己的房子', '信贷情况'] #特征标签
- return dataSet, labels #返回数据集和分类属性
-
- """
- 函数说明:按照给定特征划分数据集
-
- Parameters:
- dataSet - 待划分的数据集
- axis - 划分数据集的特征
- value - 需要返回的特征的值
- Returns:
- 无
- Author:
- Jack Cui
- Blog:
- http://blog.csdn.net/c406495762
- Modify:
- 2017-07-24
- """
- def splitDataSet(dataSet, axis, value):
- retDataSet = [] #创建返回的数据集列表
- for featVec in dataSet: #遍历数据集
- if featVec[axis] == value:
- reducedFeatVec = featVec[:axis] #去掉axis特征
- reducedFeatVec.extend(featVec[axis+1:]) #将符合条件的添加到返回的数据集
- retDataSet.append(reducedFeatVec)
- return retDataSet #返回划分后的数据集
-
- """
- 函数说明:选择最优特征
-
- Parameters:
- dataSet - 数据集
- Returns:
- bestFeature - 信息增益最大的(最优)特征的索引值
- Author:
- Jack Cui
- Blog:
- http://blog.csdn.net/c406495762
- Modify:
- 2017-07-20
- """
- def chooseBestFeatureToSplit(dataSet):
- numFeatures = len(dataSet[0]) - 1 #特征数量
- baseEntropy = calcShannonEnt(dataSet) #计算数据集的香农熵
- bestInfoGain = 0.0 #信息增益
- bestFeature = -1 #最优特征的索引值
- for i in range(numFeatures): #遍历所有特征
- #获取dataSet的第i个所有特征
- featList = [example[i] for example in dataSet]
- uniqueVals = set(featList) #创建set集合{},元素不可重复
- newEntropy = 0.0 #经验条件熵
- for value in uniqueVals: #计算信息增益
- subDataSet = splitDataSet(dataSet, i, value) #subDataSet划分后的子集
- prob = len(subDataSet) / float(len(dataSet)) #计算子集的概率
- newEntropy += prob * calcShannonEnt(subDataSet) #根据公式计算经验条件熵
- infoGain = baseEntropy - newEntropy #信息增益
- # print("第%d个特征的增益为%.3f" % (i, infoGain)) #打印每个特征的信息增益
- if (infoGain > bestInfoGain): #计算信息增益
- bestInfoGain = infoGain #更新信息增益,找到最大的信息增益
- bestFeature = i #记录信息增益最大的特征的索引值
- return bestFeature #返回信息增益最大的特征的索引值
-
-
- """
- 函数说明:统计classList中出现此处最多的元素(类标签)
-
- Parameters:
- classList - 类标签列表
- Returns:
- sortedClassCount[0][0] - 出现此处最多的元素(类标签)
- Author:
- Jack Cui
- Blog:
- http://blog.csdn.net/c406495762
- Modify:
- 2017-07-24
- """
- def majorityCnt(classList):
- classCount = {}
- for vote in classList: #统计classList中每个元素出现的次数
- if vote not in classCount.keys():classCount[vote] = 0
- classCount[vote] += 1
- sortedClassCount = sorted(classCount.items(), key = operator.itemgetter(1), reverse = True) #根据字典的值降序排序
- return sortedClassCount[0][0] #返回classList中出现次数最多的元素
-
- """
- 函数说明:创建决策树
-
- Parameters:
- dataSet - 训练数据集
- labels - 分类属性标签
- featLabels - 存储选择的最优特征标签
- Returns:
- myTree - 决策树
- Author:
- Jack Cui
- Blog:
- http://blog.csdn.net/c406495762
- Modify:
- 2017-07-25
- """
- def createTree(dataSet, labels, featLabels):
- classList = [example[-1] for example in dataSet] #取分类标签(是否放贷:yes or no)
- if classList.count(classList[0]) == len(classList): #如果类别完全相同则停止继续划分
- return classList[0]
- if len(dataSet[0]) == 1 or len(labels) == 0: #遍历完所有特征时返回出现次数最多的类标签
- return majorityCnt(classList)
- bestFeat = chooseBestFeatureToSplit(dataSet) #选择最优特征
- bestFeatLabel = labels[bestFeat] #最优特征的标签
- featLabels.append(bestFeatLabel)
- myTree = {bestFeatLabel:{}} #根据最优特征的标签生成树
- del(labels[bestFeat]) #删除已经使用特征标签
- featValues = [example[bestFeat] for example in dataSet] #得到训练集中所有最优特征的属性值
- uniqueVals = set(featValues) #去掉重复的属性值
- for value in uniqueVals: #遍历特征,创建决策树。
- subLabels = labels[:]
- myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value), subLabels, featLabels)
-
- return myTree
-
- """
- 函数说明:获取决策树叶子结点的数目
-
- Parameters:
- myTree - 决策树
- Returns:
- numLeafs - 决策树的叶子结点的数目
- Author:
- Jack Cui
- Blog:
- http://blog.csdn.net/c406495762
- Modify:
- 2017-07-24
- """
- def getNumLeafs(myTree):
- numLeafs = 0 #初始化叶子
- firstStr = next(iter(myTree)) #python3中myTree.keys()返回的是dict_keys,不在是list,所以不能使用myTree.keys()[0]的方法获取结点属性,可以使用list(myTree.keys())[0]
- secondDict = myTree[firstStr] #获取下一组字典
- for key in secondDict.keys():
- if type(secondDict[key]).__name__=='dict': #测试该结点是否为字典,如果不是字典,代表此结点为叶子结点
- numLeafs += getNumLeafs(secondDict[key])
- else: numLeafs +=1
- return numLeafs
-
- """
- 函数说明:获取决策树的层数
-
- Parameters:
- myTree - 决策树
- Returns:
- maxDepth - 决策树的层数
- Author:
- Jack Cui
- Blog:
- http://blog.csdn.net/c406495762
- Modify:
- 2017-07-24
- """
- def getTreeDepth(myTree):
- maxDepth = 0 #初始化决策树深度
- firstStr = next(iter(myTree)) #python3中myTree.keys()返回的是dict_keys,不在是list,所以不能使用myTree.keys()[0]的方法获取结点属性,可以使用list(myTree.keys())[0]
- secondDict = myTree[firstStr] #获取下一个字典
- for key in secondDict.keys():
- if type(secondDict[key]).__name__=='dict': #测试该结点是否为字典,如果不是字典,代表此结点为叶子结点
- thisDepth = 1 + getTreeDepth(secondDict[key])
- else: thisDepth = 1
- if thisDepth > maxDepth: maxDepth = thisDepth #更新层数
- return maxDepth
-
- """
- 函数说明:绘制结点
-
- Parameters:
- nodeTxt - 结点名
- centerPt - 文本位置
- parentPt - 标注的箭头位置
- nodeType - 结点格式
- Returns:
- 无
- Author:
- Jack Cui
- Blog:
- http://blog.csdn.net/c406495762
- Modify:
- 2017-07-24
- """
- def plotNode(nodeTxt, centerPt, parentPt, nodeType):
- arrow_args = dict(arrowstyle="<-") #定义箭头格式
- font = FontProperties(fname=r"c:\windows\fonts\simsunb.ttf", size=14) #设置中文字体
- createPlot.ax1.annotate(nodeTxt, xy=parentPt, xycoords='axes fraction', #绘制结点
- xytext=centerPt, textcoords='axes fraction',
- va="center", ha="center", bbox=nodeType, arrowprops=arrow_args, FontProperties=font)
-
- """
- 函数说明:标注有向边属性值
-
- Parameters:
- cntrPt、parentPt - 用于计算标注位置
- txtString - 标注的内容
- Returns:
- 无
- Author:
- Jack Cui
- Blog:
- http://blog.csdn.net/c406495762
- Modify:
- 2017-07-24
- """
- def plotMidText(cntrPt, parentPt, txtString):
- xMid = (parentPt[0]-cntrPt[0])/2.0 + cntrPt[0] #计算标注位置
- yMid = (parentPt[1]-cntrPt[1])/2.0 + cntrPt[1]
- createPlot.ax1.text(xMid, yMid, txtString, va="center", ha="center", rotation=30)
-
- """
- 函数说明:绘制决策树
-
- Parameters:
- myTree - 决策树(字典)
- parentPt - 标注的内容
- nodeTxt - 结点名
- Returns:
- 无
- Author:
- Jack Cui
- Blog:
- http://blog.csdn.net/c406495762
- Modify:
- 2017-07-24
- """
- def plotTree(myTree, parentPt, nodeTxt):
- decisionNode = dict(boxstyle="sawtooth", fc="0.8") #设置结点格式
- leafNode = dict(boxstyle="round4", fc="0.8") #设置叶结点格式
- numLeafs = getNumLeafs(myTree) #获取决策树叶结点数目,决定了树的宽度
- depth = getTreeDepth(myTree) #获取决策树层数
- firstStr = next(iter(myTree)) #下个字典
- cntrPt = (plotTree.xOff + (1.0 + float(numLeafs))/2.0/plotTree.totalW, plotTree.yOff) #中心位置
- plotMidText(cntrPt, parentPt, nodeTxt) #标注有向边属性值
- plotNode(firstStr, cntrPt, parentPt, decisionNode) #绘制结点
- secondDict = myTree[firstStr] #下一个字典,也就是继续绘制子结点
- plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD #y偏移
- for key in secondDict.keys():
- if type(secondDict[key]).__name__=='dict': #测试该结点是否为字典,如果不是字典,代表此结点为叶子结点
- plotTree(secondDict[key],cntrPt,str(key)) #不是叶结点,递归调用继续绘制
- else: #如果是叶结点,绘制叶结点,并标注有向边属性值
- 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
-
- """
- 函数说明:创建绘制面板
-
- Parameters:
- inTree - 决策树(字典)
- Returns:
- 无
- Author:
- Jack Cui
- Blog:
- http://blog.csdn.net/c406495762
- Modify:
- 2017-07-24
- """
- def createPlot(inTree):
- fig = plt.figure(1, facecolor='white') #创建fig
- fig.clf() #清空fig
- axprops = dict(xticks=[], yticks=[])
- createPlot.ax1 = plt.subplot(111, frameon=False, **axprops) #去掉x、y轴
- plotTree.totalW = float(getNumLeafs(inTree)) #获取决策树叶结点数目
- plotTree.totalD = float(getTreeDepth(inTree)) #获取决策树层数
- plotTree.xOff = -0.5/plotTree.totalW; plotTree.yOff = 1.0; #x偏移
- plotTree(inTree, (0.5,1.0), '') #绘制决策树
- plt.show() #显示绘制结果
-
- """
- 函数说明:使用决策树分类
-
- Parameters:
- inputTree - 已经生成的决策树
- featLabels - 存储选择的最优特征标签
- testVec - 测试数据列表,顺序对应最优特征标签
- Returns:
- classLabel - 分类结果
- Author:
- Jack Cui
- Blog:
- http://blog.csdn.net/c406495762
- Modify:
- 2017-07-25
- """
- def classify(inputTree, featLabels, testVec):
- firstStr = next(iter(inputTree)) #获取决策树结点
- secondDict = inputTree[firstStr] #下一个字典
- 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
-
- """
- 函数说明:存储决策树
-
- Parameters:
- inputTree - 已经生成的决策树
- filename - 决策树的存储文件名
- Returns:
- 无
- Author:
- Jack Cui
- Blog:
- http://blog.csdn.net/c406495762
- Modify:
- 2017-07-25
- """
- def storeTree(inputTree, filename):
- with open(filename, 'wb') as fw:
- pickle.dump(inputTree, fw)
-
- """
- 函数说明:读取决策树
-
- Parameters:
- filename - 决策树的存储文件名
- Returns:
- pickle.load(fr) - 决策树字典
- Author:
- Jack Cui
- Blog:
- http://blog.csdn.net/c406495762
- Modify:
- 2017-07-25
- """
- def grabTree(filename):
- fr = open(filename, 'rb')
- return pickle.load(fr)
-
-
- if __name__ == '__main__':
- dataSet, labels = createDataSet()
- featLabels = []
- myTree = createTree(dataSet, labels, featLabels)
- # storeTree(myTree,'classifierStorage.txt')
- # newMyTree = grabTree('classifierStorage.txt');
- # createPlot(newMyTree)
- # testVec = [0, 1] # 测试数据
- # result = classify(newMyTree, featLabels, testVec)
- createPlot(myTree)
- testVec = [0,1] #测试数据
- result = classify(myTree, featLabels, testVec)
- if result == 'yes':
- print('YYYes')
- if result == 'no':
- print('NNNo')
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