Custom performance measure when building models with mlr-package

I have just made the switch from caret to mlr for a specific problem I am working on at the moment. I am wondering if anyone here is familiar with specifying custom performance measures within the resample() function.

Here's a reproducible code example:

library(mlr)
library(mlbench)

data(BostonHousing, package = "mlbench")

task_reg1  <- makeRegrTask(id = "bh", data = BostonHousing, target = "medv")
lrn_reg1   <- makeLearner(cl = "regr.randomForest",
                        predict.type = "response",
                        mtry=3) 
cv_reg1 <- makeResampleDesc("RepCV", folds = 5, reps = 5)

regr_1 <- resample(learner = lrn_reg1,
                     task = task_reg1,
                     resampling = cv_reg1,
                     measures = mlr::rmse)

Instead of computing RMSE, I want to compute the Mean Absolute Scaled Error, MASE. A function for this may, for instance, be found in the Metrics package: Metrics::mase().

I tried to include measures = Metrics::mase directly in the resample() call, but that was, as expected, a bit optimistic and I received the following error: Error in checkMeasures(measures, task) : Assertion on 'measures' failed: Must be of type 'list', not 'closure'.

I found out there's a function in the mlr package for creating custom performance metrics, called makeMeasure() (https://rdrr.io/cran/mlr/man/makeMeasure.html). I tried experimenting a bit with it, but did not manage to make anything work. I do not have much experience in tinkering with custom made functions, so I was hoping that someone here could help me out, or provide some resources for stuff like this.

Cheers!

You need to construct a function that can be applied within makeMeasure() that is of the form function(task, model, pred, extra.args). We can just write a wrapper around Metrics::mase() so you can use this function in resample(), and you can do the same for any other metric you find.

mase_fun <- function(task, model, pred, feats, extra.args) {
  Metrics::mase(pred$data$truth, pred$data$response, step_size = extra.args$step_size)
}

mase_measure <- makeMeasure(id = "mase", 
                            minimize = T,
                            properties = c("regr", "req.pred", "req.truth"),
                            fun = mase_fun,
                            extra.args = list(step_size = 1))

resample(learner = lrn_reg1,
         task = task_reg1,
         resampling = cv_reg1,
         measures = mase_measure)