目前,插入式列车在引擎盖下使用了kernlab svm功能,这些目的很慢。但是e1071 svm训练器提供了急需的速度提升。因此,我希望与e1071的svm培训师一起使用插件的cv程序。有没有办法做到这一点?基本上我希望从默认的kernlab中用e1071替换插入符号的svm引擎。
我目前使用以下代码进行训练。
svm使用kernlab
svmModel2 = train(factor(TopPick) ~. - Date , data = trainSet, method = 'svmRadial')
pred.svm2 = predict(svmModel2, testSet)
svm使用e1071
svmModel = e1071::svm(factor(TopPick) ~ . - Date, data = trainSet)
pred.svm = predict(svmModel, testSet)
感谢您的帮助。
答案 0 :(得分:3)
根据评论中的建议,您可以创建自己的自定义模型。
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),]
)
用法:
svmR <- caret::train(x = trainingSet$x,
y = trainingSet$y,
trControl = caret::trainControl(number=10),
method = svmRadial2ModelInfo,
tuneLength = 3)