simplify

R/embedded_ensemble_fselect.R

@@ -65,30 +65,15 @@ embedded_ensemble_fselect = function(
   assert_choice(class(init_resampling)[1], choices = c("ResamplingBootstrap", "ResamplingSubsampling"))
   assert_flag(store_benchmark_result)
 
-  # activate predict sets
-  walk(learners, function(learner) {
-    learner$predict_sets = c("test", "internal_valid")
-  })
-
-  # set up the task and resampling for each iteration
   init_resampling$instantiate(task)
-  grid = map_dtr(seq(init_resampling$iters), function(i) {
-    task_train = task$clone()$filter(init_resampling$train_set(i))
-    task_valid = task$clone()$filter(init_resampling$test_set(i))
-    task_train$internal_valid_task = task_valid
-    resampling = rsmp("insample")$instantiate(task_train)
 
-    data.table(
-      resampling_iteration = i,
-      learner_id = map_chr(learners, "id"),
-      learner = learners,
-      task = list(task_train),
-      resampling = list(resampling)
-    )
-  })
+  grid = benchmark_grid(
+    task = task,
+    learners = learners,
+    resamplings = init_resampling
+  )
 
-  design = grid[, c("learner", "task", "resampling"), with = FALSE]
-  bmr = benchmark(design, store_models = TRUE)
+  bmr = benchmark(grid, store_models = TRUE)
 
   trained_learners = bmr$score()$learner
 
@@ -100,19 +85,24 @@ embedded_ensemble_fselect = function(
   # extract n_features
   n_features = map_int(features, length)
 
-  measure$predict_sets = "internal_valid"
-  scores_internal = bmr$score(measure)
+  # performance scores
+  scores = bmr$score(measure)
+
+  set(scores, j = "features", value = features)
+  set(scores, j = "n_features", value = n_features)
+  setnames(scores, "iteration", "resampling_iteration")
 
-  set(grid, j = "features", value = features)
-  set(grid, j = "n_features", value = n_features)
-  set(grid, j = measure$id, value = scores_internal[[measure$id]])
-  set(grid, j = "learner", value = NULL)
   # remove R6 objects
-  set(grid, j = "task", value = NULL)
-  set(grid, j = "resampling", value = NULL)
+  set(scores, j = "learner", value = NULL)
+  set(scores, j = "task", value = NULL)
+  set(scores, j = "resampling", value = NULL)
+  set(scores, j = "prediction_test", value = NULL)
+  set(scores, j = "task_id", value = NULL)
+  set(scores, j = "nr", value = NULL)
+  set(scores, j = "resampling_id", value = NULL)
 
   EnsembleFSResult$new(
-    result = grid,
+    result = scores,
     features = task$feature_names,
     benchmark_result = if (store_benchmark_result) bmr,
     measure_id = measure$id,