R语言,ID4.5算法，实现离散型与连续型数据决策树构建及打印

本人的第一篇文章，趁着我们的数据挖掘课设的时间，把实现的决策树代码，拿出来分享下。有很多漏洞和缺陷，还有很多骇客思想的成分，但是总之，能实现，看网上的代码，能用的其实也没几个。废话不多说，直接看代码

特别鸣谢博主skyonefly的代码
附上链接：R语言决策树代码

##########   ID4.5算法实现决策树##################################################Part1 基础函数########################################################计算香农熵calShannonEnt <- function(dataSet,labels){  numEntries<-length(dataSet[,labels])   key<-rep("a",numEntries)   for(i in 1:numEntries)     key[i]<-dataSet[i,labels]   shannonEnt<-0   prob<-table(key)/numEntries   for(i in 1:length(prob))     shannonEnt=shannonEnt-prob[i]*log(prob[i],2)   return(shannonEnt)}#划分数据集splitDataSet <- function(dataSet,axis,value,tempSet = dataSet){  retDataSet = NULL  for(i in 1:nrow(dataSet)){    if(dataSet[i,axis] == value){      tempDataSet = tempSet[i,]      retDataSet = rbind(retDataSet,tempDataSet)    }  }  rownames(retDataSet) = NULL  return (retDataSet)}#选择信息增益最大的内部节点chooseBestFeatureToSplita <- function(dataSet,labels, bestInfoGain){  numFeatures = ncol(dataSet) - 1  baseEntropy = calShannonEnt(dataSet,labels)  #最大信息增益  bestFeature = -1   for(i in  1: numFeatures){    featureLabels = levels(factor(dataSet[,i]))    # featureLabels = as.numeric(featureLabels)    newEntropy = 0.0    SplitInfo = 0.0    for( j in 1:length(featureLabels)){      subDataSet = splitDataSet(dataSet,i,featureLabels[j])      prob = length(subDataSet[,1])*1.0/nrow(dataSet)      newEntropy = newEntropy + prob*calShannonEnt(subDataSet,labels)      SplitInfo = -prob*log2(prob) + SplitInfo    }    infoGain = baseEntropy - newEntropy    GainRadio = infoGain/SplitInfo    if(SplitInfo > 0){      GainRadio = infoGain/SplitInfo      if(GainRadio > bestInfoGain){        bestInfoGain = infoGain        bestFeature = i      }    }  }  return (bestFeature)} #返回频数最高的列标签majorityCnt <- function(classList){  classCount = NULL  count = as.numeric(table(classList))  majorityList = levels(as.factor(classList))  if(length(count) == 1){    return (majorityList[1])  }else{    f = max(count)    return (majorityList[which(count == f)][1])  }}#判断类标签是否只有一个因子水平oneValue <- function(classList){  count = as.numeric(table(classList))  if(length(count) == 1){    return (TRUE)  }else    return (FALSE)}#树的打印printTree <- function(tree){  df <- data.frame()  col <- 1  point <- c()  count <- 0  for(i in 1:length(tree)){    if(rownames(tree)[[i]] == 'labelFeature'){      df[i,col] = tree[[i]]      col = col + 1      count = count + 1      point[count] = col      names(point)[count] = tree[[i]]    }else if(rownames(tree)[[i]] == 'FeatureValue'){      for(j in 1:length(point)){        len = grep(names(point)[j],tree[[i]])        if(length(len) >= 1){          col = point[j]        }      }      df[i,col] = tree[[i]]      col = col + 1    }else{      df[i,col] = tree[[i]]      col = col + 1    }  }  for(i in 1:nrow(df)){    for(j in 1:ncol(df)){      if(is.na(df[i,j])){        df[i,j] = ""      }    }  }  return(df)}numericCol <- NULL#加载数据load <- function(filePath){  dataSet<-read.table(filePath,header = T)  dataSet = dataSet[,-1]  numericCol <<- c()  for(i in 1:length(ncol(dataSet))){    if(is.numeric(dataSet[,i])){      numericCol[i] <<- colnames(dataSet)[i]    }  }  dataSet = as.matrix(dataSet)  trainSet = dataSet[1:14,]  testSet = dataSet[15:21,]  preSet = dataSet[22,,drop=FALSE]  result = list(trainSet,testSet,preSet)  return (result)}#############################################Part2 连续值处理######################################################将数据集全部转换成离散型toDiscrete <- function(numericCol,dataSet,labels){  s <- c()  if(length(numericCol)>0){    for(i in 1:length(numericCol)){      if(numericCol[i] %in% colnames(dataSet)){        atr = dataSet[,numericCol[i]]        atr = as.numeric(atr)        Split = BestSplit(dataSet,numericCol[i],labels)#计算最佳分裂点        s[i] = Split        names(s)[i] = numericCol[i]        dataSet[,numericCol[i]] = toDiscreteCol(atr, Split)#将新离散型数据的写入dataSet      }    }  }  result = list(dataSet,s)  return(result)}#计算基尼系数jini <- function(data,labels){  nument<-length(data[,1])   key<-rep("a",nument)   for(i in 1:nument) {    key[i]<-data[i,labels]   }  ent<-0   prob<-table(key)/nument   for(i in 1:length(prob))     ent=ent+prob[i]*prob[i]  ent = 1 - ent  return(ent)}#找到基尼系数最小的中值，作为分裂点BestSplit <- function(dataSet,colname,labels){  numFeatures = nrow(dataSet) - 1  bestSplit = -1   sorted = sort(as.numeric(dataSet[,colname]))  atr = dataSet[,colname]  bestGini = 999  for(i in  1: numFeatures){    middle = (sorted[i] + sorted[i+1])/2    tempCol = toDiscreteCol(atr,middle)    dataSet[,colname] = tempCol    featureLabels = levels(factor(dataSet[,colname]))    Gini = 0.0    for( j in 1:length(featureLabels)){      subDataSet = splitDataSet(dataSet,colname,featureLabels[j])      prob = length(subDataSet[,1])*1.0/nrow(dataSet)      Gini = Gini + prob*jini(subDataSet,labels)    }    if(Gini <= bestGini){      bestGini = Gini      count = middle    }  }  return (count)} #将该列转换成离散型toDiscreteCol <- function(atr, Split){  str <- c()  for(i in 1:length(atr)){    if(atr[i] <= Split){      str[i] = 'a'    }else{      str[i] = 'b'    }  }  return(str)}#############################################Part3 决策树的构建####################################################递归建立生成树creatTree <- function(dataSet,labels,bestInfoGain){  result = toDiscrete(numericCol,dataSet,labels)  tempSet = dataSet  dataSet = result[[1]]  Split = result[[2]]  decision_tree = list()  classList = dataSet[,labels]  #判断是否属于同一类  if(oneValue(classList)){    label = classList[1]    return (rbind(decision_tree,label))  }  #是否在矩阵中只剩Label标签了，若只剩Label标签，则都分完了  if((ncol(dataSet) == 1)){    label = majorityCnt(classList)    decision_tree = rbind(decision_tree,labels)    return (decision_tree)  }  #选择bestFeature作为分割属性  bestFeature = chooseBestFeatureToSplita(dataSet,labels,bestInfoGain)  bestFeatureName = colnames(dataSet)[bestFeature]  #所有信息增益都小于bestInfoGain  if(bestFeature == -1){    label = majorityCnt(classList)    decision_tree = rbind(decision_tree,label)    return (decision_tree)  }  labelFeature = colnames(dataSet)[bestFeature]  #添加内部节点  decision_tree = rbind(decision_tree,labelFeature)  #选中了那个标签作为此次分类标签   attriCol = dataSet[,bestFeature]  temp_tree = data.frame()  stayData = dataSet  factor=levels(as.factor(attriCol))  for(j in 1:length(factor)){    #分裂成小数据集    dataSet = splitDataSet(stayData,bestFeature,factor[j],tempSet)    if(bestFeatureName %in% numericCol){      if(factor[j] == 'a'){        character = '<'      }else{        character = '>'      }      numpd = paste(character,Split[bestFeature])      FeatureValue = paste(bestFeatureName,numpd)    }else{      FeatureValue = paste(bestFeatureName,factor[j])    }    decision_tree = rbind(decision_tree, FeatureValue )    #删除已使用属性列    dataSet = dataSet[,-bestFeature,drop=FALSE]    #递归调用这个函数    temp_tree = creatTree(dataSet,labels,bestInfoGain)    decision_tree = rbind(decision_tree,temp_tree)  }  return (decision_tree)}#############################################Part4 预测函数########################################################算正确率test <- function(testSet,labels){  count <- 0  for(i in 1:nrow(testSet)){    pre = predict(testSet[i,,drop=FALSE],myTree)    if(is.null(pre)){    }else{      if(pre == testSet[i,labels]){        count = count + 1      }    }  }  return(count/nrow(testSet))}#打印预测结果pre <- function(testSet){  for(i in 1:nrow(testSet)){    pre = predict(testSet[i,,drop=FALSE],myTree)    return(pre)  }}#预测函数predict <- function(testSet, df, row = 1, col = 1){  if(df[row,col] == "yes"| df[row,col] == "no"){    return(df[row,col])  }else{    if(length(grep(" ",df[row,col])) == 0 & nchar(df[row,col]) > 0){      labelFeature = df[row,col]#获取属性名称      FeatureValue = testSet[,labelFeature][1]#获取属性值      if(labelFeature %in% numericCol){        count <- 1        rows <- c()        Split <- NULL        for(i in row:nrow(df)){          if(count > 2){            break          }          if(nchar(df[i,col+1]) > 0){            Split = as.numeric(strsplit(df[i,col+1]," ")[[1]][3])            rows[count] = i            count = count + 1          }        }        if(as.numeric(FeatureValue) < Split){          prediction = predict(testSet, df, rows[1] + 1, col + 2)        }else{          prediction = predict(testSet, df, rows[2] + 1, col + 2)        }        return(prediction)      }else{        sum = paste(labelFeature, FeatureValue)        for(i in row:nrow(df)){          if(df[i,col+1] == sum){            row = i            break          }        }        prediction = predict(testSet, df, row + 1, col + 2)        return(prediction)      }    }  }}###########################################Part5 加载数据并运行####################################################加载数据myData = load("C:/Users/gino2/Desktop/R/DecisiontreeSampledata.txt")#"D:/littlestar/DecisiontreeSampledata.txt"    离散型数据#"D:/littlestar/DecisiontreeSampledataContinue.txt"    连续型数据#创建决策树#三个参数：训练数据集，类标签列，增益率的阈值tree = creatTree(myData[[1]],'buys_computer',0.12)myTree <- printTree(tree)print(myTree)#预测函数rightProb = test(myData[[2]],'buys_computer')print(paste("测试集的准确率为：",rightProb))print(paste("第22行的预测结果为：",pre(myData[[3]])))

运行结果

下面是我用的数据

纯离散型

ID  age income  student credit_rating    buys_computer1    youth   high   no   fair   no2    youth   high   no   excellent  no3    middle_age      high   no   fair   yes4    senior      medium     no   fair   yes5    senior     low yes  fair   yes6    senior     low yes  excellent  no7    middle_age     low yes  excellent  yes8    youth   medium     no   fair   no9    youth  low yes  fair   yes10   senior      medium     yes  fair   yes11   youth   medium     yes  excellent  yes12   middle_age      medium     no   excellent  yes13   middle_age      high   yes  fair   yes14   senior      medium     no   excellent  no15  youth    medium     no   fair   no16  youth   low yes  excellent  yes17   middle_age      medium     yes  fair   yes18   middle_age      high   no   excellent  yes19   middle_age     low no   excellent  yes20   senior     low yes  excellent  yes21   senior     high    no   fair   no22   middle_age      medium     no   fair   NA

连续型+离散型

Id  age income  student credit_rating buys_computer1   16  high    no  fair    no2   25  high    no  excellent   no3   34  high    no  fair    yes4   41  medium  no  fair    yes5   45  low yes fair    yes6   48  low yes excellent   no7   39  low yes excellent   yes8   27  medium  no  fair    no9   25  low yes fair    yes10  49  medium  yes fair    yes11  18  medium  yes excellent   yes12  36  medium  no  excellent   yes13  38  high    yes fair    yes14  50  medium  no  excellent   no15  19  medium  no  fair    no16  25  low yes excellent   yes17  35  medium  yes fair    yes18  31  high    no  excellent   yes19  38  low no  excellent   yes20  45  low yes excellent   yes21  43  high    no  fair    no22  36  medium  no  fair    NA

在文章的最后，我要感谢给予我动力和能量去完成这篇代码的人