Merge branch 'main' of github.com:mlr-org/mlr3mbo

Author: sumny

R/ResultAssignerSurrogate.R

@@ -66,12 +66,14 @@ ResultAssignerSurrogate = R6Class("ResultAssignerSurrogate",
       extra = xydt[, !c(cols_x, cols_y), with = FALSE]
 
       # ys are still the ones originally evaluated
-      best_y = if (inherits(instance, c("OptimInstanceBatchSingleCrit", "OptimInstanceAsyncSingleCrit"))) {
-        unlist(archive$data[best, on = cols_x][, cols_y, with = FALSE])
+      if (inherits(instance, c("OptimInstanceBatchSingleCrit", "OptimInstanceAsyncSingleCrit"))) {
+        best_y = unlist(archive$data[best, on = cols_x][, cols_y, with = FALSE])
+        instance$assign_result(xdt = best, y = best_y, extra = extra)
       } else if (inherits(instance, c("OptimInstanceBatchMultiCrit", "OptimInstanceAsyncMultiCrit"))) {
-        archive$data[best, on = cols_x][, cols_y, with = FALSE]
+        best_y = archive$data[best, on = cols_x][, cols_y, with = FALSE]
+        instance$assign_result(xdt = best, ydt = best_y, extra = extra)
       }
-      instance$assign_result(xdt = best, y = best_y, extra = extra)
+
     }
   ),
 

tests/testthat/setup.R

@@ -1,7 +1,20 @@
-old_threshold_bbotk = lg$threshold
-lg$set_threshold("warn")
+old_opts = options(
+  warnPartialMatchArgs = TRUE,
+  warnPartialMatchAttr = TRUE,
+  warnPartialMatchDollar = TRUE
+)
+
+# https://github.com/HenrikBengtsson/Wishlist-for-R/issues/88
+old_opts = lapply(old_opts, function(x) if (is.null(x)) FALSE else x)
 
 lg_mlr3 = lgr::get_logger("mlr3")
+lg_bbotk = lgr::get_logger("bbotk")
+lg_rush = lgr::get_logger("rush")
+
 old_threshold_mlr3 = lg_mlr3$threshold
-lg_mlr3$set_threshold("warn")
+old_threshold_bbotk = lg_bbotk$threshold
+old_threshold_rush = lg_rush$threshold
 
+lg_mlr3$set_threshold(0)
+lg_bbotk$set_threshold(0)
+lg_rush$set_threshold(0)

tests/testthat/teardown.R

@@ -1,3 +1,4 @@
-lg$set_threshold(old_threshold_bbotk)
+options(old_opts)
 lg_mlr3$set_threshold(old_threshold_mlr3)
-
+lg_bbotk$set_threshold(old_threshold_bbotk)
+lg_rush$set_threshold(old_threshold_rush)

tests/testthat/test_ResultAssignerArchive.R

@@ -23,7 +23,7 @@ test_that("ResultAssignerArchive works with OptimizerMbo and bayesopt_ego", {
   optimizer = opt("mbo", loop_function = bayesopt_ego, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer, result_assigner = result_assigner)
   optimizer$optimize(instance)
   expect_true(nrow(instance$archive$data) == 5L)
-  expect_data_table(instance$result, nrow = 1L)
+  expect_data_table(instance$result, nrows = 1L)
 })
 
 test_that("ResultAssignerArchive works with OptimizerMbo and bayesopt_parego", {

tests/testthat/test_ResultAssignerSurrogate.R

@@ -46,7 +46,7 @@ test_that("ResultAssignerSurrogate works with OptimizerMbo and bayesopt_ego", {
 
   expect_r6(result_assigner$surrogate, classes = "SurrogateLearner")
   expect_r6(result_assigner$surrogate$learner, classes = "Learner")
-  expect_data_table(instance$result, nrow = 1L)
+  expect_data_table(instance$result, nrows = 1L)
 })
 
 test_that("ResultAssignerSurrogate works with OptimizerMbo and bayesopt_parego", {

tests/testthat/test_SurrogateLearnerCollection.R

@@ -47,7 +47,7 @@ test_that("predict_types are recognized", {
   learner1$predict_type = "se"
   learner2 = lrn("regr.rpart")
   learner2$predict_type = "response"
-  surrogate = SurrogateLearnerCollection$new(learner = list(learner1, learner2), archive = inst$archive)
+  surrogate = SurrogateLearnerCollection$new(learners = list(learner1, learner2), archive = inst$archive)
   surrogate$update()
 
   xdt = data.table(x = seq(-1, 1, length.out = 5L))
@@ -58,7 +58,7 @@ test_that("predict_types are recognized", {
 
 test_that("param_set", {
   inst = MAKE_INST(OBJ_1D_2, PS_1D, trm("evals", n_evals = 5L))
-  surrogate = SurrogateLearnerCollection$new(learner = list(REGR_FEATURELESS, REGR_FEATURELESS$clone(deep = TRUE)), archive = inst$archive)
+  surrogate = SurrogateLearnerCollection$new(learners = list(REGR_FEATURELESS, REGR_FEATURELESS$clone(deep = TRUE)), archive = inst$archive)
   expect_r6(surrogate$param_set, "ParamSet")
   expect_setequal(surrogate$param_set$ids(), c("assert_insample_perf", "perf_measures", "perf_thresholds", "catch_errors", "impute_method"))
   expect_equal(surrogate$param_set$class[["assert_insample_perf"]], "ParamLgl")
@@ -77,7 +77,7 @@ test_that("insample_perf", {
   design = MAKE_DESIGN(inst)
   inst$eval_batch(design)
 
-  surrogate = SurrogateLearnerCollection$new(learner = list(REGR_KM_DETERM, REGR_KM_DETERM$clone(deep = TRUE)), archive = inst$archive)
+  surrogate = SurrogateLearnerCollection$new(learners = list(REGR_KM_DETERM, REGR_KM_DETERM$clone(deep = TRUE)), archive = inst$archive)
   expect_error({surrogate$insample_perf = c(0, 0)}, regexp = "insample_perf is read-only.")
   expect_error({surrogate$assert_insample_perf = 0}, regexp = "assert_insample_perf is read-only.")
 
@@ -91,7 +91,7 @@ test_that("insample_perf", {
   expect_double(surrogate$insample_perf, lower = -Inf, upper = 1, any.missing = FALSE, len = 2L)
   expect_equal(names(surrogate$insample_perf), map_chr(surrogate$param_set$values$perf_measures, "id"))
 
-  surrogate_constant = SurrogateLearnerCollection$new(learner = list(REGR_FEATURELESS, REGR_FEATURELESS$clone(deep = TRUE)), archive = inst$archive)
+  surrogate_constant = SurrogateLearnerCollection$new(learners = list(REGR_FEATURELESS, REGR_FEATURELESS$clone(deep = TRUE)), archive = inst$archive)
   surrogate_constant$param_set$values$assert_insample_perf = TRUE
   surrogate_constant$param_set$values$perf_thresholds = c(0.5, 0.5)
   surrogate_constant$param_set$values$perf_measures = list(mlr_measures$get("regr.rsq"), mlr_measures$get("regr.rsq"))

tests/testthat/test_TunerADBO.R

@@ -13,7 +13,7 @@ test_that("TunerADBO works", {
     task = tsk("pima"),
     learner = learner,
     resampling = rsmp("cv", folds = 3L),
-    measure = msr("classif.ce"),
+    measures = msr("classif.ce"),
     terminator = trm("evals", n_evals = 20L),
     store_benchmark_result = FALSE
   )

tests/testthat/test_TunerAsyncMbo.R

@@ -13,7 +13,7 @@ test_that("TunerAsyncMbo works", {
     task = tsk("pima"),
     learner = learner,
     resampling = rsmp("cv", folds = 3L),
-    measure = msr("classif.ce"),
+    measures = msr("classif.ce"),
     terminator = trm("evals", n_evals = 20L),
     store_benchmark_result = FALSE
   )

tests/testthat/test_TunerMbo.R

@@ -157,7 +157,7 @@ test_that("TunerMbo reset", {
   instance = TuningInstanceBatchSingleCrit$new(tsk("iris"), learner = learner, resampling = rsmp("holdout"), measure = msr("classif.ce"), terminator = trm("evals", n_evals = 5L))
   tuner$optimize(instance)
 
-  instance_mult = TuningInstanceBatchMultiCrit$new(tsk("iris"), learner = learner, resampling = rsmp("holdout"), measure = msrs(c("classif.ce", "classif.logloss")), terminator = trm("evals", n_evals = 5L))
+  instance_mult = TuningInstanceBatchMultiCrit$new(tsk("iris"), learner = learner, resampling = rsmp("holdout"), measures = msrs(c("classif.ce", "classif.logloss")), terminator = trm("evals", n_evals = 5L))
 
   expect_error(tuner$optimize(instance_mult), "does not support multi-crit objectives")
   expect_loop_function(tuner$loop_function)