Add timeout field to learners (#556)

Author: mllg

DESCRIPTION

@@ -68,7 +68,7 @@ Imports:
     lgr (>= 0.3.4),
     mlbench,
     mlr3measures (>= 0.3.0),
-    mlr3misc (>= 0.5.0),
+    mlr3misc (>= 0.6.0),
     paradox (>= 0.4.0),
     uuid
 Suggests:

R/Learner.R

@@ -120,6 +120,13 @@ Learner = R6Class("Learner",
     #' @template field_predict_sets
     predict_sets = "test",
 
+    #' @field timeout (named `numeric(2)`)\cr
+    #' Timeout for the learner's train and predict steps, in seconds.
+    #' This works differently for different encapsulation methods, see
+    #' [mlr3misc::encapsulate()].
+    #' Default is `c(train = Inf, predict = Inf)`.
+    timeout = c(train = Inf, predict = Inf),
+
     #' @field fallback ([Learner])\cr
     #' Learner which is fitted to impute predictions in case that either the model fitting or the prediction of the top learner is not successful.
     #' Requires you to enable encapsulation, otherwise errors are not caught and the execution is terminated before the fallback learner kicks in.

R/worker.R

@@ -46,7 +46,8 @@ learner_train = function(learner, task, row_ids = NULL) {
     .f = train_wrapper,
     .args = list(learner = learner, task = task),
     .pkgs = learner$packages,
-    .seed = NA_integer_
+    .seed = NA_integer_,
+    .timeout = learner$timeout["train"]
   )
 
   learner$state = insert_named(learner$state, list(
@@ -142,7 +143,8 @@ learner_predict = function(learner, task, row_ids = NULL) {
       .f = predict_wrapper,
       .args = list(task = task, learner = learner),
       .pkgs = learner$packages,
-      .seed = NA_integer_
+      .seed = NA_integer_,
+      .timeout = learner$timeout["predict"]
     )
 
     prediction = result$result