在R中实施禁忌搜索

Question

我试图在分类数据集上实施禁忌搜索，即在UCI存储库中可用的https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset)上的印度患者肝脏疾病，但是面临着问题。 以下是我使用的代码

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) 

数据的某些部分

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") 

如果有人可以帮我一下吧

Answer 1

我想看看禁忌如何工作，所以似乎是一个很好的开始。

基本上你需要更好地测试你的代码，evaluate只是没有工作。通过创建th的值然后调用evaluate就可以轻松地进行测试。

还可以使用高级评论来组织您的代码并跟踪您正在做什么，尤其是在发布到SO时寻求帮助，以便节省我们时间来弄清楚您的意图。

不确定这些结果是否好，数据量是如此之小以至于很难说。

反正这里是改变的代码：

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) 

以下是输出结果：

[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 

，这里是你的阴谋：