refactor: extract internal tuned values in instance (#164)

* refactor: extract internal tuned values in instance

* ...

* ...

DESCRIPTION

@@ -41,13 +41,13 @@ BugReports: https://github.com/mlr-org/mlr3mbo/issues
 Depends:
     R (>= 3.1.0)
 Imports:
-    bbotk (>= 1.0.0),
+    bbotk (>= 1.1.1),
     checkmate (>= 2.0.0),
     data.table,
     lgr (>= 0.3.4),
     mlr3 (>= 0.21.0),
     mlr3misc (>= 0.11.0),
-    mlr3tuning (>= 1.0.0),
+    mlr3tuning (>= 1.0.2),
     paradox (>= 1.0.0),
     spacefillr,
     R6 (>= 2.4.1)

R/ResultAssignerArchive.R

@@ -29,15 +29,15 @@ ResultAssignerArchive = R6Class("ResultAssignerArchive",
     #' @param instance ([bbotk::OptimInstanceBatchSingleCrit] | [bbotk::OptimInstanceBatchMultiCrit])\cr
     #'   The [bbotk::OptimInstance] the final result should be assigned to.
     assign_result = function(instance) {
-      res = instance$archive$best()
-      xdt = res[, instance$search_space$ids(), with = FALSE]
+      xydt = instance$archive$best()
+      xdt = xydt[, instance$search_space$ids(), with = FALSE]
       if (inherits(instance, "OptimInstanceBatchMultiCrit")) {
-        ydt = res[, instance$archive$cols_y, with = FALSE]
-        instance$assign_result(xdt, ydt)
+        ydt = xydt[, instance$archive$cols_y, with = FALSE]
+        instance$assign_result(xdt, ydt, xydt = xydt)
       }
       else {
-        y = unlist(res[, instance$archive$cols_y, with = FALSE])
-        instance$assign_result(xdt, y)
+        y = unlist(xydt[, instance$archive$cols_y, with = FALSE])
+        instance$assign_result(xdt, y, xydt = xydt)
       }
     }
   ),

R/ResultAssignerSurrogate.R

@@ -59,15 +59,16 @@ ResultAssignerSurrogate = R6Class("ResultAssignerSurrogate",
       }
       archive_tmp = archive$clone(deep = TRUE)
       archive_tmp$data[, self$surrogate$cols_y := means]
-      best = archive_tmp$best()[, archive_tmp$cols_x, with = FALSE]
+      xydt = archive_tmp$best()
+      best = xydt[, archive_tmp$cols_x, with = FALSE]
 
       # ys are still the ones originally evaluated
       best_y = if (inherits(instance, "OptimInstanceBatchSingleCrit")) {
         unlist(archive$data[best, on = archive$cols_x][, archive$cols_y, with = FALSE])
       } else if (inherits(instance, "OptimInstanceBatchMultiCrit")) {
         archive$data[best, on = archive$cols_x][, archive$cols_y, with = FALSE]
       }
-      instance$assign_result(xdt = best, best_y)
+      instance$assign_result(xdt = best, best_y, xydt = xydt)
     }
   ),
 

tests/testthat/test_ResultAssignerArchive.R

@@ -54,3 +54,31 @@ test_that("ResultAssignerArchive works with OptimizerMbo and bayesopt_smsego", {
   expect_data_table(instance$result, min.rows = 1L)
 })
 
+test_that("ResultAssignerArchive passes internal tuned values", {
+  result_assigner = ResultAssignerArchive$new()
+
+  learner = lrn("classif.debug",
+    validate = 0.2,
+    early_stopping = TRUE,
+    x = to_tune(0.2, 0.3),
+    iter = to_tune(upper = 1000, internal = TRUE, aggr = function(x) 99))
+
+  instance = ti(
+    task = tsk("pima"),
+    learner = learner,
+    resampling = rsmp("cv", folds = 3),
+    measures = msr("classif.ce"),
+    terminator = trm("evals", n_evals = 20),
+    store_benchmark_result = TRUE
+  )
+  surrogate = SurrogateLearner$new(REGR_KM_DETERM)
+  acq_function = AcqFunctionEI$new()
+  acq_optimizer = AcqOptimizer$new(opt("random_search", batch_size = 2L), terminator = trm("evals", n_evals = 2L))
+
+  tuner = tnr("mbo", result_assigner = result_assigner)
+  expect_data_table(tuner$optimize(instance), nrows = 1)
+  expect_list(instance$archive$data$internal_tuned_values, len = 20, types = "list")
+  expect_equal(instance$archive$data$internal_tuned_values[[1]], list(iter = 99))
+  expect_false(instance$result_learner_param_vals$early_stopping)
+  expect_equal(instance$result_learner_param_vals$iter, 99)
+})

tests/testthat/test_ResultAssignerSurrogate.R

@@ -85,3 +85,31 @@ test_that("ResultAssignerSurrogate works with OptimizerMbo and bayesopt_smsego",
   expect_data_table(instance$result, min.rows = 1L)
 })
 
+test_that("ResultAssignerSurrogate passes internal tuned values", {
+  result_assigner = ResultAssignerSurrogate$new()
+
+  learner = lrn("classif.debug",
+    validate = 0.2,
+    early_stopping = TRUE,
+    x = to_tune(0.2, 0.3),
+    iter = to_tune(upper = 1000, internal = TRUE, aggr = function(x) 99))
+
+  instance = ti(
+    task = tsk("pima"),
+    learner = learner,
+    resampling = rsmp("cv", folds = 3),
+    measures = msr("classif.ce"),
+    terminator = trm("evals", n_evals = 20),
+    store_benchmark_result = TRUE
+  )
+  surrogate = SurrogateLearner$new(REGR_KM_DETERM)
+  acq_function = AcqFunctionEI$new()
+  acq_optimizer = AcqOptimizer$new(opt("random_search", batch_size = 2L), terminator = trm("evals", n_evals = 2L))
+
+  tuner = tnr("mbo", result_assigner = result_assigner)
+  expect_data_table(tuner$optimize(instance), nrows = 1)
+  expect_list(instance$archive$data$internal_tuned_values, len = 20, types = "list")
+  expect_equal(instance$archive$data$internal_tuned_values[[1]], list(iter = 99))
+  expect_false(instance$result_learner_param_vals$early_stopping)
+  expect_equal(instance$result_learner_param_vals$iter, 99)
+})