python实现C4.5决策树算法

Python  /  管理员 发布于 7年前   173

C4.5算法使用信息增益率来代替ID3的信息增益进行特征的选择，克服了信息增益选择特征时偏向于特征值个数较多的不足。信息增益率的定义如下：

# -*- coding: utf-8 -*-from numpy import *import mathimport copyimport cPickle as pickleclass C45DTree(object): def __init__(self): # 构造方法  self.tree = {} # 生成树  self.dataSet = [] # 数据集  self.labels = [] # 标签集 # 数据导入函数 def loadDataSet(self, path, labels):  recordList = []  fp = open(path, "rb") # 读取文件内容  content = fp.read()  fp.close()  rowList = content.splitlines() # 按行转换为一维表  recordList = [row.split("\t") for row in rowList if row.strip()] # strip()函数删除空格、Tab等  self.dataSet = recordList  self.labels = labels # 执行决策树函数 def train(self):  labels = copy.deepcopy(self.labels)  self.tree = self.buildTree(self.dataSet, labels) # 构件决策树：穿件决策树主程序 def buildTree(self, dataSet, lables):  cateList = [data[-1] for data in dataSet] # 抽取源数据集中的决策标签列  # 程序终止条件1：如果classList只有一种决策标签，停止划分，返回这个决策标签  if cateList.count(cateList[0]) == len(cateList):   return cateList[0]  # 程序终止条件2：如果数据集的第一个决策标签只有一个，返回这个标签  if len(dataSet[0]) == 1:   return self.maxCate(cateList)  # 核心部分  bestFeat, featValueList= self.getBestFeat(dataSet) # 返回数据集的最优特征轴  bestFeatLabel = lables[bestFeat]  tree = {bestFeatLabel: {}}  del (lables[bestFeat])  for value in featValueList: # 决策树递归生长   subLables = lables[:] # 将删除后的特征类别集建立子类别集   # 按最优特征列和值分隔数据集   splitDataset = self.splitDataSet(dataSet, bestFeat, value)   subTree = self.buildTree(splitDataset, subLables) # 构建子树   tree[bestFeatLabel][value] = subTree  return tree # 计算出现次数最多的类别标签 def maxCate(self, cateList):  items = dict([(cateList.count(i), i) for i in cateList])  return items[max(items.keys())] # 计算最优特征 def getBestFeat(self, dataSet):  Num_Feats = len(dataSet[0][:-1])  totality = len(dataSet)  BaseEntropy = self.computeEntropy(dataSet)  ConditionEntropy = []  # 初始化条件熵  slpitInfo = [] # for C4.5,caculate gain ratio  allFeatVList = []  for f in xrange(Num_Feats):   featList = [example[f] for example in dataSet]   [splitI, featureValueList] = self.computeSplitInfo(featList)   allFeatVList.append(featureValueList)   slpitInfo.append(splitI)   resultGain = 0.0   for value in featureValueList:    subSet = self.splitDataSet(dataSet, f, value)    appearNum = float(len(subSet))    subEntropy = self.computeEntropy(subSet)    resultGain += (appearNum/totality)*subEntropy   ConditionEntropy.append(resultGain) # 总条件熵  infoGainArray = BaseEntropy*ones(Num_Feats)-array(ConditionEntropy)  infoGainRatio = infoGainArray/array(slpitInfo) # C4.5信息增益的计算  bestFeatureIndex = argsort(-infoGainRatio)[0]  return bestFeatureIndex, allFeatVList[bestFeatureIndex] # 计算划分信息 def computeSplitInfo(self, featureVList):  numEntries = len(featureVList)  featureVauleSetList = list(set(featureVList))  valueCounts = [featureVList.count(featVec) for featVec in featureVauleSetList]  pList = [float(item)/numEntries for item in valueCounts]  lList = [item*math.log(item, 2) for item in pList]  splitInfo = -sum(lList)  return splitInfo, featureVauleSetList # 计算信息熵 # @staticmethod def computeEntropy(self, dataSet):  dataLen = float(len(dataSet))  cateList = [data[-1] for data in dataSet] # 从数据集中得到类别标签  # 得到类别为key、 出现次数value的字典  items = dict([(i, cateList.count(i)) for i in cateList])  infoEntropy = 0.0  for key in items: # 香农熵： = -p*log2(p) --infoEntropy = -prob * log(prob, 2)   prob = float(items[key]) / dataLen   infoEntropy -= prob * math.log(prob, 2)  return infoEntropy # 划分数据集： 分割数据集； 删除特征轴所在的数据列，返回剩余的数据集 # dataSet : 数据集； axis: 特征轴； value: 特征轴的取值 def splitDataSet(self, dataSet, axis, value):  rtnList = []  for featVec in dataSet:   if featVec[axis] == value:    rFeatVec = featVec[:axis] # list操作：提取0~（axis-1）的元素    rFeatVec.extend(featVec[axis + 1:]) # 将特征轴之后的元素加回    rtnList.append(rFeatVec)  return rtnList # 存取树到文件 def storetree(self, inputTree, filename):  fw = open(filename,'w')  pickle.dump(inputTree, fw)  fw.close() # 从文件抓取树 def grabTree(self, filename):  fr = open(filename)  return pickle.load(fr)

调用代码

# -*- coding: utf-8 -*-from numpy import *from C45DTree import *dtree = C45DTree()dtree.loadDataSet("dataset.dat",["age", "revenue", "student", "credit"])dtree.train()dtree.storetree(dtree.tree, "data.tree")mytree = dtree.grabTree("data.tree")print mytree

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