if (is.null(params))
# Here, gmodel is a string (= its family),
# because a custom model must be given with its parameters.
- params <- as.list(private$getParams(gmodel, data, target))
+ params <- as.list(private$getParams(gmodel, data, target, task))
private$params <- params
if (is.character(gmodel))
gmodel <- private$getGmodel(gmodel, task)
function(dataHO, targetHO, param) {
require(rpart)
method <- ifelse(task == "classification", "class", "anova")
+ if (is.null(colnames(dataHO)))
+ colnames(dataHO) <- paste0("V", 1:ncol(dataHO))
df <- data.frame(cbind(dataHO, target=targetHO))
model <- rpart::rpart(target ~ ., df, method=method, control=list(cp=param))
- function(X) predict(model, X)
+ function(X) {
+ if (is.null(colnames(X)))
+ colnames(X) <- paste0("V", 1:ncol(X))
+ predict(model, as.data.frame(X))
+ }
}
}
else if (family == "rf") {
}
},
# Return a default list of parameters, given a gmodel family
- getParams = function(family, data, target) {
+ getParams = function(family, data, target, task) {
if (family == "tree") {
# Run rpart once to obtain a CV grid for parameter cp
require(rpart)
df <- data.frame(cbind(data, target=target))
ctrl <- list(
+ cp = 0,
minsplit = 2,
minbucket = 1,
- maxcompete = 0,
- maxsurrogate = 0,
- usesurrogate = 0,
- xval = 0,
- surrogatestyle = 0,
- maxdepth = 30)
- r <- rpart(target ~ ., df, method="class", control=ctrl)
+ xval = 0)
+ method <- ifelse(task == "classification", "class", "anova")
+ r <- rpart(target ~ ., df, method=method, control=ctrl)
cps <- r$cptable[-1,1]
+ if (length(cps) <= 1)
+ stop("No cross-validation possible: select another model")
if (length(cps) <= 11)
return (cps)
step <- (length(cps) - 1) / 10