How do I save models as a vector with a loop? - r

Question

So I have this assignment where I have to create 3 different models (r). I can do them individually without a problem. However I want to take it a step further and to create a function that trains all of them with a for loop. (I know I could create a function that trained the 3 models each time. I am not looking for other solutions to the problem, I want to do it this way (or in a similar fashion) because now I have 3 models but imagine if I wanted to train 20!
I tried creating a list to store all three models, but i keep having some warnings.
library(caret)
library(readr)
library(rstudioapi)
library(e1071)
library(dplyr)
library(rpart)
TrainingFunction <- function(method,formula,data,tune) {
fitcontrol <- trainControl(method = "repeatedcv", repeats = 4)
if(method == "rf") {Model <- train(formula, data = data,method = method, trcontrol = fitcontrol , tunelenght = tune)}
else if (method == "knn"){
preObj <- preProcess(data[, c(13,14,15)], method=c("center", "scale"))
data <- predict(preObj, data)
Model <- train(formula, data = data,method = method, trcontrol = fitcontrol , tunelenght = tune)
}
else if (method == "svm"){Model <- svm(formula, data = data,cost=1000 , gamma = 0.001)}
Model
}
So this is a training function I created, and it works, but now I want to train all three at once !
So I tried this:
methods <- c("rf","knn","svm")
Models <- vector(mode = "list" , length = length(methods))
for(i in 1:length(methods))
{Models[i] <- TrainingFunction(methods[i],Volume~.,List$trainingSet,5)}
This are the warnings :
Warning messages:
1: In Models[i] <- TrainingFunction(methods[i], Volume ~ ., List$trainingSet, :
number of items to replace is not a multiple of replacement length
2: In Models[i] <- TrainingFunction(methods[i], Volume ~ ., List$trainingSet, :
number of items to replace is not a multiple of replacement length
3: In svm.default(x, y, scale = scale, ..., na.action = na.action) :
Variable(s) ‘ProductType.GameConsole’ constant. Cannot scale data.
4: In Models[i] <- TrainingFunction(methods[i], Volume ~ ., List$trainingSet, :
number of items to replace is not a multiple of replacement length
When I do Models the output is this :
[[1]]
[1] "rf"
[[2]]
[1] "knn"
[[3]]
svm(formula = formula, data = data, cost = 1000, gamma = 0.001)

Answer 1

Consider switch to avoid the many if and else especially if extending to 20 models. Then use lapply to build a list without initialization or iterative assignment:
TrainingFunction <- function(method, formula, data, tune) {
fitcontrol <- trainControl(method = "repeatedcv", repeats = 4)
Model <- switch(method,
"rf" = train(formula, data = data, method = method,
trcontrol = fitcontrol, tunelength = tune)
"knn" = {
preObj <- preProcess(data[,c(13,14,15)],
method=c("center", "scale"))
data <- predict(preObj, data)
train(formula, data = data, method = method,
trcontrol = fitcontrol, tunelength = tune)
}
"svm" = svm(formula, data = data, cost = 1000, gamma = 0.001)
)
}
methods <- c("rf","knn","svm")
Model_list <-lapply(methods, function(m)
TrainingFunction(m, Volume~., List$trainingSet, 5))

Answer 2

I think the problem comes from this line:
{Models[i] <- TrainingFunction(methods[i],Volume~.,List$trainingSet,5)}
If you want to assign your model to the i-th place of the list, you should do it with a double bracket, like this:
{Models[[i]] <- TrainingFunction(methods[i],Volume~.,List$trainingSet,5)}
Another alternative would be use lapply instead of an explicit loop, so you avoid that problem altogether:
train_from_method <- function(methods) {TrainingFunction(methods,Volume~.,List$trainingSet,5)}
Models <- lapply(species_vector, train_from_method)