决策树剪枝算法的python实现方法详解

def calcShannonEnt(dataSet):   numEntries = len(dataSet)   labelCounts = {}   # 给所有可能分类创建字典   for featVec in dataSet:     currentLabel = featVec[-1]     if currentLabel not in labelCounts.keys():       labelCounts[currentLabel] = 0    labelCounts[currentLabel] += 1  shannonEnt = 0.0  # 以2为底数计算香农熵  for key in labelCounts:    prob = float(labelCounts[key]) / numEntries    shannonEnt -= prob * log(prob, 2)  return shannonEnt

# 对离散变量划分数据集，取出该特征取值为value的所有样本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

  numFeatures = len(dataSet[0]) - 1  baseEntropy = calcShannonEnt(dataSet)  bestInfoGain = 0.0  bestFeature = -1  bestSplitDict = {}  for i in range(numFeatures):    featList = [example[i] for example in dataSet]    # 对连续型特征进行处理    if type(featList[0]).__name__ == 'float' or type(featList[0]).__name__ == 'int':      # 产生n-1个候选划分点      sortfeatList = sorted(featList)      splitList = []      for j in range(len(sortfeatList) - 1):        splitList.append((sortfeatList[j] + sortfeatList[j + 1]) / 2.0)      bestSplitEntropy = 10000      slen = len(splitList)      # 求用第j个候选划分点划分时，得到的信息熵，并记录最佳划分点      for j in range(slen):        value = splitList[j]        newEntropy = 0.0        subDataSet0 = splitContinuousDataSet(dataSet, i, value, 0)        subDataSet1 = splitContinuousDataSet(dataSet, i, value, 1)        prob0 = len(subDataSet0) / float(len(dataSet))        newEntropy += prob0 * calcShannonEnt(subDataSet0)        prob1 = len(subDataSet1) / float(len(dataSet))        newEntropy += prob1 * calcShannonEnt(subDataSet1)        if newEntropy < bestSplitEntropy:          bestSplitEntropy = newEntropy          bestSplit = j      # 用字典记录当前特征的最佳划分点      bestSplitDict[labels[i]] = splitList[bestSplit]      infoGain = baseEntropy - bestSplitEntropy    # 对离散型特征进行处理    else:      uniqueVals = set(featList)      newEntropy = 0.0      # 计算该特征下每种划分的信息熵      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  # 若当前节点的最佳划分特征为连续特征，则将其以之前记录的划分点为界进行二值化处理  # 即是否小于等于bestSplitValue  if type(dataSet[0][bestFeature]).__name__ == 'float' or type(dataSet[0][bestFeature]).__name__ == 'int':    bestSplitValue = bestSplitDict[labels[bestFeature]]    labels[bestFeature] = labels[bestFeature] + '<=' + str(bestSplitValue)    for i in range(shape(dataSet)[0]):      if dataSet[i][bestFeature] <= bestSplitValue:        dataSet[i][bestFeature] = 1      else:        dataSet[i][bestFeature] = 0  return bestFeature

def chooseBestFeatureToSplit(dataSet, labels):  numFeatures = len(dataSet[0]) - 1  baseEntropy = calcShannonEnt(dataSet)  bestInfoGain = 0.0  bestFeature = -1  bestSplitDict = {}  for i in range(numFeatures):    featList = [example[i] for example in dataSet]    # 对连续型特征进行处理    if type(featList[0]).__name__ == 'float' or type(featList[0]).__name__ == 'int':      # 产生n-1个候选划分点      sortfeatList = sorted(featList)      splitList = []      for j in range(len(sortfeatList) - 1):        splitList.append((sortfeatList[j] + sortfeatList[j + 1]) / 2.0)      bestSplitEntropy = 10000      slen = len(splitList)      # 求用第j个候选划分点划分时，得到的信息熵，并记录最佳划分点      for j in range(slen):        value = splitList[j]        newEntropy = 0.0        subDataSet0 = splitContinuousDataSet(dataSet, i, value, 0)        subDataSet1 = splitContinuousDataSet(dataSet, i, value, 1)        prob0 = len(subDataSet0) / float(len(dataSet))        newEntropy += prob0 * calcShannonEnt(subDataSet0)        prob1 = len(subDataSet1) / float(len(dataSet))        newEntropy += prob1 * calcShannonEnt(subDataSet1)        if newEntropy < bestSplitEntropy:          bestSplitEntropy = newEntropy          bestSplit = j      # 用字典记录当前特征的最佳划分点      bestSplitDict[labels[i]] = splitList[bestSplit]      infoGain = baseEntropy - bestSplitEntropy    # 对离散型特征进行处理    else:      uniqueVals = set(featList)      newEntropy = 0.0      # 计算该特征下每种划分的信息熵      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  # 若当前节点的最佳划分特征为连续特征，则将其以之前记录的划分点为界进行二值化处理  # 即是否小于等于bestSplitValue  if type(dataSet[0][bestFeature]).__name__ == 'float' or type(dataSet[0][bestFeature]).__name__ == 'int':    bestSplitValue = bestSplitDict[labels[bestFeature]]    labels[bestFeature] = labels[bestFeature] + '<=' + str(bestSplitValue)    for i in range(shape(dataSet)[0]):      if dataSet[i][bestFeature] <= bestSplitValue:        dataSet[i][bestFeature] = 1      else:        dataSet[i][bestFeature] = 0  return bestFeature``def classify(inputTree, featLabels, testVec):  firstStr = inputTree.keys()[0]  if u'<=' in firstStr:    featvalue = float(firstStr.split(u"<=")[1])    featkey = firstStr.split(u"<=")[0]    secondDict = inputTree[firstStr]    featIndex = featLabels.index(featkey)    if testVec[featIndex] <= featvalue:      judge = 1    else:      judge = 0    for key in secondDict.keys():      if judge == int(key):        if type(secondDict[key]).__name__ == 'dict':          classLabel = classify(secondDict[key], featLabels, testVec)        else:          classLabel = secondDict[key]  else:    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

def majorityCnt(classList):  classCount={}  for vote in classList:    if vote not in classCount.keys():      classCount[vote]=0    classCount[vote]+=1  return max(classCount)def testing_feat(feat, train_data, test_data, labels):  class_list = [example[-1] for example in train_data]  bestFeatIndex = labels.index(feat)  train_data = [example[bestFeatIndex] for example in train_data]  test_data = [(example[bestFeatIndex], example[-1]) for example in test_data]  all_feat = set(train_data)  error = 0.0  for value in all_feat:    class_feat = [class_list[i] for i in range(len(class_list)) if train_data[i] == value]    major = majorityCnt(class_feat)    for data in test_data:      if data[0] == value and data[1] != major:        error += 1.0  # print 'myTree %d' % error  return error

  error = 0.0  for i in range(len(data_test)):    if classify(myTree, labels, data_test[i]) != data_test[i][-1]:      error += 1  # print 'myTree %d' % error  return float(error)def testingMajor(major, data_test):  error = 0.0  for i in range(len(data_test)):    if major != data_test[i][-1]:      error += 1  # print 'major %d' % error  return float(error)**递归产生决策树**```def createTree(dataSet,labels,data_full,labels_full,test_data,mode):  classList=[example[-1] for example in dataSet]  if classList.count(classList[0])==len(classList):    return classList[0]  if len(dataSet[0])==1:    return majorityCnt(classList)  labels_copy = copy.deepcopy(labels)  bestFeat=chooseBestFeatureToSplit(dataSet,labels)  bestFeatLabel=labels[bestFeat]  if mode == "unpro" or mode == "post":    myTree = {bestFeatLabel: {}}  elif mode == "prev":    if testing_feat(bestFeatLabel, dataSet, test_data, labels_copy) < testingMajor(majorityCnt(classList),                                            test_data):      myTree = {bestFeatLabel: {}}    else:      return majorityCnt(classList)  featValues=[example[bestFeat] for example in dataSet]  uniqueVals=set(featValues)  if type(dataSet[0][bestFeat]).__name__ == 'unicode':    currentlabel = labels_full.index(labels[bestFeat])    featValuesFull = [example[currentlabel] for example in data_full]    uniqueValsFull = set(featValuesFull)  del (labels[bestFeat])  for value in uniqueVals:    subLabels = labels[:]    if type(dataSet[0][bestFeat]).__name__ == 'unicode':      uniqueValsFull.remove(value)    myTree[bestFeatLabel][value] = createTree(splitDataSet /                           (dataSet, bestFeat, value), subLabels, data_full, labels_full,                         splitDataSet /                           (test_data, bestFeat, value), mode=mode)  if type(dataSet[0][bestFeat]).__name__ == 'unicode':    for value in uniqueValsFull:      myTree[bestFeatLabel][value] = majorityCnt(classList)  if mode == "post":    if testing(myTree, test_data, labels_copy) > testingMajor(majorityCnt(classList), test_data):      return majorityCnt(classList)  return myTree<div class="se-preview-section-delimiter"></div>```**读入数据**```def load_data(file_name):  with open(r"dd.csv", 'rb') as f:   df = pd.read_csv(f,sep=",")   print(df)   train_data = df.values[:11, 1:].tolist()  print(train_data)  test_data = df.values[11:, 1:].tolist()  labels = df.columns.values[1:-1].tolist()  return train_data, test_data, labels<div class="se-preview-section-delimiter"></div>```测试并绘制树图import matplotlib.pyplot as pltdecisionNode = dict(boxstyle="round4", color='red') # 定义判断结点形态leafNode = dict(boxstyle="circle", color='grey') # 定义叶结点形态arrow_args = dict(arrowstyle="<-", color='blue') # 定义箭头# 计算树的叶子节点数量def getNumLeafs(myTree):  numLeafs = 0  firstSides = list(myTree.keys())  firstStr = firstSides[0]  secondDict = myTree[firstStr]  for key in secondDict.keys():    if type(secondDict[key]).__name__ == 'dict':      numLeafs += getNumLeafs(secondDict[key])    else:      numLeafs += 1  return numLeafs# 计算树的最大深度def getTreeDepth(myTree):  maxDepth = 0  firstSides = list(myTree.keys())  firstStr = firstSides[0]  secondDict = myTree[firstStr]  for key in secondDict.keys():    if type(secondDict[key]).__name__ == 'dict':      thisDepth = 1 + getTreeDepth(secondDict[key])    else:      thisDepth = 1    if thisDepth > maxDepth:      maxDepth = thisDepth  return maxDepth# 画节点def plotNode(nodeTxt, centerPt, parentPt, nodeType):  createPlot.ax1.annotate(nodeTxt, xy=parentPt, xycoords='axes fraction', /              xytext=centerPt, textcoords='axes fraction', va="center", ha="center", /              bbox=nodeType, arrowprops=arrow_args)# 画箭头上的文字def plotMidText(cntrPt, parentPt, txtString):  lens = len(txtString)  xMid = (parentPt[0] + cntrPt[0]) / 2.0 - lens * 0.002  yMid = (parentPt[1] + cntrPt[1]) / 2.0  createPlot.ax1.text(xMid, yMid, txtString)def plotTree(myTree, parentPt, nodeTxt):  numLeafs = getNumLeafs(myTree)  depth = getTreeDepth(myTree)  firstSides = list(myTree.keys())  firstStr = firstSides[0]  cntrPt = (plotTree.x0ff + (1.0 + float(numLeafs)) / 2.0 / plotTree.totalW, plotTree.y0ff)  plotMidText(cntrPt, parentPt, nodeTxt)  plotNode(firstStr, cntrPt, parentPt, decisionNode)  secondDict = myTree[firstStr]  plotTree.y0ff = plotTree.y0ff - 1.0 / plotTree.totalD  for key in secondDict.keys():    if type(secondDict[key]).__name__ == 'dict':      plotTree(secondDict[key], cntrPt, str(key))    else:      plotTree.x0ff = plotTree.x0ff + 1.0 / plotTree.totalW      plotNode(secondDict[key], (plotTree.x0ff, plotTree.y0ff), cntrPt, leafNode)      plotMidText((plotTree.x0ff, plotTree.y0ff), cntrPt, str(key))  plotTree.y0ff = plotTree.y0ff + 1.0 / plotTree.totalDdef createPlot(inTree):  fig = plt.figure(1, facecolor='white')  fig.clf()  axprops = dict(xticks=[], yticks=[])  createPlot.ax1 = plt.subplot(111, frameon=False, **axprops)  plotTree.totalW = float(getNumLeafs(inTree))  plotTree.totalD = float(getTreeDepth(inTree))  plotTree.x0ff = -0.5 / plotTree.totalW  plotTree.y0ff = 1.0  plotTree(inTree, (0.5, 1.0), '')  plt.show()

if __name__ == "__main__":  train_data, test_data, labels = load_data("dd.csv")  data_full = train_data[:]  labels_full = labels[:]  mode="post"  mode = "prev"  mode="post"  myTree = createTree(train_data, labels, data_full, labels_full, test_data, mode=mode)  createPlot(myTree)  print(json.dumps(myTree, ensure_ascii=False, indent=4))

if __name__ == "__main__":  train_data, test_data, labels = load_data("dd.csv")  data_full = train_data[:]  labels_full = labels[:]  mode="post"  mode = "prev"  mode="post"  myTree = createTree(train_data, labels, data_full, labels_full, test_data, mode=mode)  createPlot(myTree)  print(json.dumps(myTree, ensure_ascii=False, indent=4))