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)
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))
+ if (task == "regression")
+ type <- "vector"
+ else {
+ if (is.null(dim(targetHO)))
+ type <- "class"
+ else
+ type <- "prob"
+ }
function(X) {
if (is.null(colnames(X)))
colnames(X) <- paste0("V", 1:ncol(X))
- predict(model, as.data.frame(X))
+ predict(model, as.data.frame(X), type=type)
}
}
}
}
},
# 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)
minsplit = 2,
minbucket = 1,
xval = 0)
- r <- rpart(target ~ ., df, method="class", control=ctrl)
+ method <- ifelse(task == "classification", "class", "anova")
+ r <- rpart(target ~ ., df, method=method, control=ctrl)
cps <- r$cptable[-1,1]
- if (length(cps) <= 11) {
- if (length(cps == 0))
- stop("No cross-validation possible: select another model")
+ if (length(cps) <= 1)
+ stop("No cross-validation possible: select another model")
+ if (length(cps) <= 11)
return (cps)
- }
step <- (length(cps) - 1) / 10
cps[unique(round(seq(1, length(cps), step)))]
}
p <- ncol(data)
# Use caret package to obtain the CV grid of mtry values
require(caret)
- caret::var_seq(p, classification = (task == "classificaton"),
+ caret::var_seq(p, classification = (task == "classification"),
len = min(10, p-1))
}
else if (family == "ppr")