Implementing Tabu Search in R - r

Question

I am trying to implement Tabu Search on a classification dataset namely Indian patients liver disease available in the UCI repository on https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset) but facing issues.
Following is the code I've used
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)
Error:
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)
Some part of the data
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")
If anyone could help me with it

Answer 1

I wanted to see how tabu worked anyway so seemed a good place to start.
Basically you need to test your code better, evaluate just did not work. It is easy to test by hand by creating values of th and then calling evaluate on them.
Also use high level comments to organize your code and keep track of what you are doing, especially when posting to SO for help so as to save us time figuring out what you intend.
Not sure if these results are good, the amount of data is so minimal it is hard to tell.
Anyway here is the changed code:
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)
Here are the output results:
[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
And here is your plot: