python实现ID3决策树算法

Python  /  管理员 发布于 7年前   275

ID3决策树是以信息增益作为决策标准的一种贪心决策树算法

# -*- coding: utf-8 -*-from numpy import *import mathimport copyimport cPickle as pickleclass ID3DTree(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 = self.getBestFeat(dataSet) # 返回数据集的最优特征轴    bestFeatLabel = lables[bestFeat]    tree = {bestFeatLabel: {}}    del (lables[bestFeat])    # 抽取最优特征轴的列向量    uniqueVals = set([data[bestFeat] for data in dataSet]) # 去重    for value in uniqueVals: # 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):    # 计算特征向量维，其中最后一列用于类别标签    numFeatures = len(dataSet[0]) - 1 # 特征向量维数=行向量维数-1    baseEntropy = self.computeEntropy(dataSet) # 基础熵    bestInfoGain = 0.0 # 初始化最优的信息增益    bestFeature = -1 # 初始化最优的特征轴    # 外循环：遍历数据集各列，计算最优特征轴    # i为数据集列索引：取值范围0~(numFeatures-1)    for i in xrange(numFeatures):      uniqueVals = set([data[i] for data in dataSet]) # 去重      newEntropy = 0.0      for value in uniqueVals:        subDataSet = self.splitDataSet(dataSet, i, value)        prob = len(subDataSet) / float(len(dataSet))        newEntropy += prob * self.computeEntropy(subDataSet)      infoGain = baseEntropy - newEntropy      if (infoGain > bestInfoGain): # 信息增益大于0        bestInfoGain = infoGain # 用当前信息增益值替代之前的最优增益值        bestFeature = i # 重置最优特征为当前列    return bestFeature  # 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:])        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 ID3DTree import *dtree = ID3DTree()# ["age", "revenue", "student", "credit"]对应年龄、收入、学生、信誉4个特征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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