SVM in R with caret using e1071 instead of kernlab - r

Question

Currently the caret train uses kernlab svm function under the hood and these are slow for my current purpose. But e1071 svm trainers offer a much needed speed boost. So I would like the cv procedure of caret with svm trainers of e1071. Is there any way to do that? Basically I want the svm engine of caret to be replaced by e1071 from the default kernlab.
I use the following code to train currently.
svm using kernlab
svmModel2 = train(factor(TopPick) ~. - Date , data = trainSet, method = 'svmRadial')
pred.svm2 = predict(svmModel2, testSet)
svm using e1071
svmModel = e1071::svm(factor(TopPick) ~ . - Date, data = trainSet)
pred.svm = predict(svmModel, testSet)
Thanks for the help.

Answer 1

As suggested in comment you can create your own custom model.
svmRadial2ModelInfo <- list(
label = "Support Vector Machines with Radial Kernel based on libsvm",
library = "e1071",
type = c("Regression", "Classification"),
parameters = data.frame(parameter = c("cost", "gamma"),
class = c("numeric", "numeric"),
label = c("Cost", "Gamma")),
grid = function(x, y, len = NULL, search = NULL) {
sigmas <- kernlab::sigest(as.matrix(x), na.action = na.omit, scaled = TRUE)
return( expand.grid(gamma = mean(as.vector(sigmas[-2])),
cost = 2 ^((1:len) - 3)) )
},
loop = NULL,
fit = function(x, y, wts, param, lev, last, classProbs, ...) {
if(any(names(list(...)) == "probability") | is.numeric(y))
{
out <- svm(x = as.matrix(x), y = y,
kernel = "radial",
cost = param$cost,
gamma = param$gamma,
...)
} else {
out <- svm(x = as.matrix(x), y = y,
kernel = "radial",
cost = param$cost,
gamma = param$gamma,
probability = classProbs,
...)
}
out
},
predict = function(modelFit, newdata, submodels = NULL) {
predict(modelFit, newdata)
},
prob = function(modelFit, newdata, submodels = NULL) {
out <- predict(modelFit, newdata, probability = TRUE)
attr(out, "probabilities")
},
varImp = NULL,
predictors = function(x, ...){
out <- if(!is.null(x$terms)) predictors.terms(x$terms) else x$xNames
if(is.null(out)) out <- names(attr(x, "scaling")$x.scale$`scaled:center`)
if(is.null(out)) out <-NA
out
},
levels = function(x) x$levels,
sort = function(x) x[order(x$cost, -x$gamma),]
)
Usage:
svmR <- caret::train(x = trainingSet$x,
y = trainingSet$y,
trControl = caret::trainControl(number=10),
method = svmRadial2ModelInfo,
tuneLength = 3)