tabnet variable importance output not compatible with autotest expectation #12
Description
Currently the importance() function merely extracts values from .$model$fit$importances.
Autotest expects the names of the importance scores to be the same as the input feature names, which is perfectly reasonable except for when tabnet gives separate logicalTRUE and logicalFALSE scores for a feature logical.
library(mlr3)
library(mlr3torch)
# Load test scaffolding without helper_debugging.R
lapply(list.files(system.file("testthat", package = "mlr3"), pattern = "^helper.*\\.[rR]$", full.names = TRUE)[-2], source)
learner <- lrn("classif.torch.tabnet")
# Extracted from run_experiment()
tasks <- generate_tasks(learner, N = 30L)
# Pick the test which fails
task <- tasks[["feat_all_binary"]]
lrn = lrn("classif.torch.tabnet", epochs = 1)
lrn$train(task)
# tabnet gives two importance scores per logical variable value
lrn$importance()
#> integer ordered numeric logicalTRUE logicalFALSE factor
#> 0.644666921 0.241514186 0.082143583 0.028178666 0.002125061 0.001371584
task$feature_names
#> [1] "factor" "integer" "logical" "numeric" "ordered"
# Different learner result for reference ----------------------------------
library(mlr3learners)
lrnranger <- lrn("classif.ranger", importance = "impurity_corrected")
lrnranger$train(task)
lrnranger$importance()
#> factor logical integer ordered numeric
#> -0.004777695 -0.034839272 -0.123387319 -0.146356698 -0.974573206
task$feature_names
#> [1] "factor" "integer" "logical" "numeric" "ordered"Created on 2021-12-13 by the reprex package (v2.0.1)