resample() does not set data_prototype (and task_prototype), which some learners rely on

[zecojls]

resample() does not set data_prototype (and task_prototype), which some learners rely on

[be-marc]

Hey, sorry I can't reproduce the issue. I create a clean environment with renv.

renv::init(bare = TRUE)
renv::install(c("[email protected]", "mlr-org/mlr3extralearners@*release", "randomForest"))

Your code runs without any problems.

task = tsk("boston_housing")
task$select(c("age", "b", "chas")) 
learner = lrn("regr.randomForest", importance = "mse")
learner$train(task)
rr = resample(task, learner, rsmp("cv", folds = 10))

Session info.

R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 23.10

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8      
 [8] LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Europe/Berlin
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
[1] mlr3extralearners_0.7.1 mlr3_0.17.1            

loaded via a namespace (and not attached):
 [1] digest_0.6.33        backports_1.4.1      R6_2.5.1             codetools_0.2-19     randomForest_4.7-1.1 lgr_0.4.4            parallel_4.3.1       RhpcBLASctl_0.23-42  palmerpenguins_0.1.1
[10] mlr3misc_0.13.0      parallelly_1.36.0    pak_0.7.1            future_1.33.1        renv_1.0.3           data.table_1.14.10   compiler_4.3.1       paradox_0.11.1       globals_0.16.2   
``

[zecojls]

My Kaggle kernel has R 4.0 and the Ubuntu 20 installed by default. Not sure if I can change that. What do you recommend?

[be-marc]

I can confirm that there is a bug on kaggle. It is not the subsetting of the task and not the task itself. The error does not occur with regr.rpart but with regr.randomForest and regr.ranger. I cannot reproduce the bug on my local machine or in a rocker image with R 4.0.5. The error looks like mlr3 is not passing data to the predict function of the upstream packages. Such an error would definitely have been noticed in our unit tests. Yes, that is quite tricky now. We can't debug easily on Kaggle.

[be-marc]

https://www.kaggle.com/bemarc7832/issue-987