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)
+})