feat: add debug messages (#136)

* feat: add debug messages

* ...

Author: be-marc

DESCRIPTION

@@ -24,10 +24,10 @@ URL: https://mlr3fselect.mlr-org.com,
     https://github.com/mlr-org/mlr3fselect
 BugReports: https://github.com/mlr-org/mlr3fselect/issues
 Depends:
-    mlr3 (>= 0.21.1),
+    mlr3 (>= 0.23.0.9000),
     R (>= 3.1.0)
 Imports:
-    bbotk (>= 1.5.0),
+    bbotk (>= 1.5.0.9000),
     checkmate (>= 2.0.0),
     data.table,
     lgr,
@@ -43,6 +43,9 @@ Suggests:
     rpart,
     fastVoteR,
     testthat (>= 3.0.0)
+Remotes:
+    mlr-org/mlr3,
+    mlr-org/bbotk
 Config/testthat/edition: 3
 Config/testthat/parallel: true
 Encoding: UTF-8

R/ObjectiveFSelectBatch.R

@@ -58,6 +58,8 @@ ObjectiveFSelectBatch = R6Class("ObjectiveFSelectBatch",
     .eval_many = function(xss, resampling) {
       private$.xss = xss
 
+     lg$trace("Evaluating hyperparameter configuration %s", as_short_string(xs))
+
       tasks = map(private$.xss, function(x) {
         state = self$task$feature_names[unlist(x)]
         task = self$task$clone()
@@ -71,13 +73,19 @@ ObjectiveFSelectBatch = R6Class("ObjectiveFSelectBatch",
       private$.design = data.table(task = tasks, learner = list(self$learner), resampling = resampling)
       call_back("on_eval_after_design", self$callbacks, self$context)
 
+      lg$debug("Resampling feature subsets")
+
       # learner is already cloned, task is internally cloned by PipeOpSelect, and resampling is not changed
       private$.benchmark_result = benchmark(private$.design, store_models = self$store_models || private$.model_required, clone = character())
       call_back("on_eval_after_benchmark", self$callbacks, self$context)
 
+      lg$debug("Aggregating performance")
+
       # aggregate performance scores
       private$.aggregated_performance = private$.benchmark_result$aggregate(self$measures, conditions = TRUE)[, c(self$codomain$target_ids, "warnings", "errors"), with = FALSE]
 
+      lg$debug("Aggregated performance %s", as_short_string(private$.aggregated_performance))
+
       # add runtime to evaluations
       time = map_dbl(private$.benchmark_result$resample_results$resample_result, function(rr) {
         sum(map_dbl(get_private(rr)$.data$learner_states(get_private(rr)$.view), function(state) state$train_time + state$predict_time))
@@ -86,6 +94,7 @@ ObjectiveFSelectBatch = R6Class("ObjectiveFSelectBatch",
 
       # store benchmark result in archive
       if (self$store_benchmark_result) {
+        lg$debug("Storing resample result")
         self$archive$benchmark_result$combine(private$.benchmark_result)
         set(private$.aggregated_performance, j = "uhash", value = private$.benchmark_result$uhashes)
       }

R/zzz.R

@@ -29,7 +29,7 @@
   x$add("mlr3fselect.one_se_rule", load_callback_one_se_rule)
   x$add("mlr3fselect.internal_tuning", load_callback_internal_tuning)
 
-  assign("lg", lgr::get_logger("bbotk"), envir = parent.env(environment()))
+  assign("lg", lgr::get_logger("mlr3/bbotk"), envir = parent.env(environment()))
   if (Sys.getenv("IN_PKGDOWN") == "true") {
     lg$set_threshold("warn")
   }