基于Python实现的ID3决策树功能示例

# coding=utf-8import operatorfrom math import logimport timedef createDataSet():  dataSet = [[1, 1, 'yes'],        [1, 1, 'yes'],        [1, 0, 'no'],        [0, 1, 'no'],        [0, 1, 'no'],        [0,0,'maybe']]  labels = ['no surfaceing', 'flippers']  return dataSet, labels# 计算香农熵def calcShannonEnt(dataSet):  numEntries = len(dataSet)  labelCounts = {}  for feaVec in dataSet:    currentLabel = feaVec[-1]    if currentLabel not in labelCounts:      labelCounts[currentLabel] = 0    labelCounts[currentLabel] += 1  shannonEnt = 0.0  for key in labelCounts:    prob = float(labelCounts[key]) / numEntries    shannonEnt -= prob * log(prob, 2)  return shannonEntdef 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 retDataSetdef chooseBestFeatureToSplit(dataSet):  numFeatures = len(dataSet[0]) - 1 # 因为数据集的最后一项是标签  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# 因为我们递归构建决策树是根据属性的消耗进行计算的，所以可能会存在最后属性用完了，但是分类# 还是没有算完，这时候就会采用多数表决的方式计算节点分类def majorityCnt(classList):  classCount = {}  for vote in classList:    if vote not in classCount.keys():      classCount[vote] = 0    classCount[vote] += 1  return max(classCount)def createTree(dataSet, labels):  classList = [example[-1] for example in dataSet]  if classList.count(classList[0]) == len(classList): # 类别相同则停止划分    return classList[0]  if len(dataSet[0]) == 1: # 所有特征已经用完    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[:] # 为了不改变原始列表的内容复制了一下    myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet,                                bestFeat, value), subLabels)  return myTreedef main():  data, label = createDataSet()  t1 = time.clock()  myTree = createTree(data, label)  t2 = time.clock()  print myTree  print 'execute for ', t2 - t1if __name__ == '__main__':  main()

{'no surfaceing': {0: {'flippers': {0: 'maybe', 1: 'no'}}, 1: {'flippers': {0: 'no', 1: 'yes'}}}}execute for 0.0103958394532