Question

我正在尝试在分类数据集上实施Tabu搜索，即在https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset)的UCI存储库中提供的印度患者肝病但面临问题。以下是我用过的代码

NF <- 10
NTR <- 193
NTE <- 193
library(class) 
library(e1071)
library(caret)
library(party)
library(nnet)
ILPD <- read.csv("C:/Users/Dell/Desktop/Codes and Datasets/ILPD.csv")
nrow(ILPD)  
set.seed(9850)
gp<-runif(nrow(ILPD))
ILPD<-ILPD[order(gp),]
idx <- createDataPartition(y = ILPD$Class, p = 0.7, list = FALSE)
train<-ILPD[idx,]
test<-ILPD[-idx,]
ver<-test[,11]
evaluate <- function(th){ 
if (sum(th) == 0)return(0)             
model <- svm(train[ ,th==1], train[,11] , gamma = 0.1, kernel ="sigmoid", na.action = na.omit)
pred <- predict(model, test[ ,th==1])
csRate <- sum(pred == ver)/NTE 
penalty <- (NF - sum(th))/NF 
return(csRate + penalty)
}  
library(tabuSearch)
res <- tabuSearch(size = NF, iters = 2, objFunc = evaluate, config =     matrix(1,1,NF), listSize = 5, nRestarts = 4) 
plot(res)
plot(res, "tracePlot")
summary(res, verbose = TRUE)

错误：

Error in if (any(co)) { : missing value where TRUE/FALSE needed
In addition: Warning message:
In FUN(newX[, i], ...) : NAs introduced by coercion
Called from: svm.default(train[, th == 1], train[, 11], gamma = 0.1, kernel = "sigmoid", na.action = na.omit)

部分数据

structure(list(age = c(55L, 48L, 14L, 17L, 40L, 37L), gender = c(0L, 
0L, 0L, 0L, 1L, 0L), TB = c(0.9, 2.4, 0.9, 0.9, 0.9, 0.7), DB = c(0.2, 
1.1, 0.3, 0.2, 0.3, 0.2), Alkphos = c(116L, 554L, 310L, 224L, 
293L, 235L), SGPT = c(36L, 141L, 21L, 36L, 232L, 96L), sgot = c(16L, 
73L, 16L, 45L, 245L, 54L), TP = c(6.2, 7.5, 8.1, 6.9, 6.8, 9.5
), ALB = c(3.2, 3.6, 4.2, 4.2, 3.1, 4.9), AG = c(1, 0.9, 1, 1.55, 
0.8, 1), Class = structure(c(2L, 1L, 2L, 1L, 1L, 1L), .Label = c("One", 
"Two"), class = "factor")), .Names = c("age", "gender", "TB", 
"DB", "Alkphos", "SGPT", "sgot", "TP", "ALB", "AG", "Class"), row.names =    c(216L, 
405L, 316L, 103L, 20L, 268L), class = "data.frame")

如果有人可以帮助我

Answer 1

我想知道禁忌是如何运作的，所以这似乎是一个好的起点。

基本上你需要更好地测试你的代码，th只是没有用。通过创建evaluate的值然后在其上调用NF <- 10 NTR <- 193 NTE <- 193 library(class) library(e1071) library(caret) library(party) library(nnet) ILPD1 <- structure( list( age = c(55L,48L,14L,17L,40L,37L), gender = c(0L,0L,0L,0L,1L,0L), TB = c(0.9,2.4,0.9,0.9,0.9,0.7), DB = c(0.2,1.1,0.3,0.2,0.3,0.2), Alkphos = c(116L,554L,310L,224L,293L,235L), SGPT = c(36L,141L,21L,36L,232L,96L), sgot = c(16L,73L,16L,45L,245L,54L), TP = c(6.2,7.5,8.1,6.9,6.8,9.5), ALB = c(3.2,3.6,4.2,4.2,3.1,4.9), AG = c(1,0.9,1,1.55,0.8,1), Class = structure(c(2L,1L,2L,1L,1L,1L), .Label = c("One","Two"), class = "factor") ), .Names = c("age","gender","TB","DB","Alkphos", "SGPT","sgot","TP","ALB","AG","Class"), row.names = c(216L,405L,316L,103L,20L,268L), class = "data.frame" ) ILPD <- ILPD1 #ILPD <- read.csv("ILPD.csv") nrow(ILPD) set.seed(9850) # setup test and training data gp <- runif(nrow(ILPD)) ILPD <- ILPD[order(gp),] idx <- createDataPartition(y = ILPD$Class,p = 0.7,list = FALSE) train <- ILPD[idx,] test <- ILPD[ - idx,] ver <- test[,11] evaluate <- function(th) { # evaluate the tabu for a value of th # tabuSearch will use this function to evaluate points in its search space # # if everything is turned off just return zero as we are not interested if(sum(th) == 0) return(0) # we just want to train our svm on the columns for which th==1 svmtrn <- train[,th==1] # but we need to have the Class varible as our label if (is.null(trn$Class)) return(0) # Train up an svm now # Note that the first argument is the forumula we are training model <- svm(Class~.,svmtrn,gamma = 0.1,kernel = "sigmoid",na.action = na.omit) pred <- predict(model,test) # now evaluate how well our prediction worked csRate <- sum(pred == ver) / NTE penalty <- (NF - sum(th)) / NF return(csRate + penalty) } library(tabuSearch) evaluate(matrix(1,1,NF)) res <- tabuSearch(size = NF,iters = 2,objFunc = evaluate, config = matrix(1,1,NF),listSize = 5,nRestarts = 4) plot(res) plot(res,"tracePlot") summary(res,verbose = TRUE)，可以轻松手动测试。

还可以使用高级注释来组织代码并跟踪您正在做的事情，尤其是在向SO发帖寻求帮助时，以便节省我们计算您的意图的时间。

不确定这些结果是否合适，数据量是如此之小，很难说清楚。

无论如何这里是改变的代码：

[1] 6
[1] 0.005181347
Tabu Settings
  Type                                       = binary configuration
  No of algorithm repeats                    = 1
  No of iterations at each prelim search     = 2
  Total no of iterations                     = 12
  No of unique best configurations           = 8
  Tabu list size                             = 5
  Configuration length                       = 10
  No of neighbours visited at each iteration = 10
Results:
  Highest value of objective fn    = 0.70518
  Occurs # of times                = 1
  Optimum number of variables      = 3
Optimum configuration: 
 [1] 1 0 0 0 0 1 0 0 0 1

以下是输出结果：

[
    {
        _id: ObjectId('myId1'),
        probes: ['id_probe_1', 'id_probe_2']
    },
    {
        _id: ObjectId('myId2'),
        probes: ['id_probe_1', 'id_probe_3']
    }
]

这是你的情节：

在R中实现禁忌搜索

1 个答案: