CODEWITHSUNDEEP
rsvm
gtnbz2nyt
gtnbz2nyt

Reputation: 1535

Error with custom SVM model for tuning in caret

I'm trying to follow this link to create a custom SVM and run it through some cross-validations. My primary reason for this is to run Sigma, Cost and Epsilon parameters in my grid-search and the closest caret model (svmRadial) can only do two of those.

When I attempt to run the code below, I get the following error all over the place at every iteration of my grid:

Warning in eval(expr, envir, enclos) :
  model fit failed for Fold1.: sigma=0.2, C=2, epsilon=0.1 Error in if (!isS4(modelFit) & !(method$label %in% c("Ensemble Partial Least Squares Regression",  : 
  argument is of length zero

Even when I replicate the code from the link verbatim, I get a similar error and I'm not sure how to solve it. I found this link which goes through how the custom models are built and I see where this error is referenced, but still not sure what the issue is. I have my code below:

#Generate Tuning Criteria across Parameters
C <- c(1,2)
sigma <- c(0.1,.2)
epsilon <- c(0.1,.2)
grid <- data.frame(C,sigma)

#Parameters
prm <- data.frame(parameter = c("C", "sigma","epsilon"),
                  class = rep("numeric", 3),
                  label = c("Cost", "Sigma", "Epsilon"))

#Tuning Grid
svmGrid <- function(x, y, len = NULL) {
    expand.grid(sigma = sigma,
                C = C,
                epsilon = epsilon)
}

#Fit Element Function
svmFit <- function(x, y, wts, param, lev, last, weights, classProbs, ...) {
    ksvm(x = as.matrix(x), y = y,
         type = "eps-svr",
         kernel = rbfdot,
         kpar = list(sigma = param$sigma),
         C = param$C,
         epsilon = param$epsilon,
         prob.model = classProbs,
         ...)
}

#Predict Element Function
svmPred <- function(modelFit, newdata, preProc = NULL, submodels = NULL)
    predict(modelFit, newdata)

#Sort Element Function
svmSort <- function(x) x[order(x$C),]

#Model
newSVM <- list(type="Regression",
               library="kernlab",
               loop = NULL,
               parameters = prm,
               grid = svmGrid,
               fit = svmFit,
               predict = svmPred,
               prob = NULL,
               sort = svmSort,
               levels = NULL)                    


#Train 
tc<-trainControl("repeatedcv",number=2, repeats = 0,
                 verboseIter = T,savePredictions=T)
svmCV <- train(
    Y~ 1
    + X1
    + X2
    ,data = data_nn,
    method=newSVM, 
    trControl=tc
    ,preProc = c("center","scale"))                 
svmCV

Upvotes: 1

Views: 1086

Answers (1)

gtnbz2nyt
gtnbz2nyt

Reputation: 1535

After viewing the second link provided, I decided to try and include a label into the Model's parameters and that solved the issue! It's funny that it worked because the caret documentation says that value is optional, but if it works I can't complain.

#Model 
newSVM <- list(label="My Model",
                   type="Regression",
                   library="kernlab",
                   loop = NULL,
                   parameters = prm,
                   grid = svmGrid,
                   fit = svmFit,
                   predict = svmPred,
                   prob = NULL,
                   sort = svmSort,
                   levels = NULL)

Upvotes: 0

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