refactor: add option to store prototype

mlr-org · Dec 7, 2023 · 2164d4d · 2164d4d
1 parent 2c77828
commit 2164d4d
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Showing 2 changed files with 8 additions and 9 deletions.
diff --git a/R/Learner.R b/R/Learner.R
@@ -242,7 +242,7 @@ Learner = R6Class("Learner",
       train_row_ids = if (!is.null(row_ids)) row_ids else task$row_roles$use
       test_row_ids = task$row_roles$test
 
-      learner_train(learner, task, train_row_ids = train_row_ids, test_row_ids = test_row_ids, mode = mode)
+      learner_train(learner, task, train_row_ids = train_row_ids, test_row_ids = test_row_ids, mode = mode, store_prototype = TRUE)
 
       # store the task w/o the data
       self$state$train_task = task_rm_backend(task$clone(deep = TRUE))

diff --git a/R/worker.R b/R/worker.R
@@ -1,4 +1,4 @@
-learner_train = function(learner, task, train_row_ids = NULL, test_row_ids = NULL, mode = "train") {
+learner_train = function(learner, task, train_row_ids = NULL, test_row_ids = NULL, mode = "train", store_prototype = FALSE) {
   # This wrapper calls learner$train, and additionally performs some basic
   # checks that the training was successful.
   # Exceptions here are possibly encapsulated, so that they get captured
@@ -68,18 +68,21 @@ learner_train = function(learner, task, train_row_ids = NULL, test_row_ids = NUL
   log = append_log(NULL, "train", result$log$class, result$log$msg)
   train_time = result$elapsed
 
-  proto = task$data(rows = integer())
   learner$state = insert_named(learner$state, list(
     model = result$result,
     log = log,
     train_time = train_time,
     param_vals = learner$param_set$values,
     task_hash = task$hash,
-    data_prototype = proto,
-    task_prototype = proto,
     mlr3_version = mlr_reflections$package_version
   ))
 
+  if (store_prototype) {
+    proto = task$data(rows = integer()))
+    learner$state$data_prototype = proto
+    learner$state$task_prototype = proto
+  }
+
   if (is.null(result$result)) {
     lg$debug("Learner '%s' on task '%s' failed to %s a model",
       learner$id, task$id, mode, learner = learner$clone(), messages = result$log$msg)
@@ -249,10 +252,6 @@ workhorse = function(iteration, task, learner, resampling, param_values = NULL,
   learner_hash = learner$hash
   learner = learner_train(learner, task, sets[["train"]], sets[["test"]], mode = mode)
 
-  # repeated saving of the prototype leads to large ResultData objects if the task contains many columns, factor levels or attributes
-  learner$state$data_prototype = NULL
-  learner$state$task_prototype = NULL
-
   # predict for each set
   sets = sets[learner$predict_sets]
   pdatas = Map(function(set, row_ids) {