feat: add resample stages to tuning callbacks

DESCRIPTION

@@ -50,6 +50,9 @@ Suggests:
     rpart,
     testthat (>= 3.0.0),
     xgboost
+Remotes:
+    mlr-org/mlr3,
+    mlr-org/bbotk
 VignetteBuilder:
     knitr
 Config/testthat/edition: 3

R/ArchiveAsyncTuning.R

@@ -170,8 +170,7 @@ ArchiveAsyncTuning = R6Class("ArchiveAsyncTuning",
       # cache benchmark result
       if (self$rush$n_finished_tasks > private$.benchmark_result$n_resample_results) {
         bmrs = map(self$finished_data$resample_result, as_benchmark_result)
-        init = BenchmarkResult$new()
-        private$.benchmark_result = Reduce(function(lhs, rhs) lhs$combine(rhs), bmrs, init = init)
+        private$.benchmark_result = Reduce(function(lhs, rhs) lhs$combine(rhs), bmrs)
       }
       private$.benchmark_result
     }

R/CallbackAsyncTuning.R

@@ -1,4 +1,4 @@
-#' @title Create Asynchronous Tuning Callback
+#' @title Asynchronous Tuning Callback
 #'
 #' @description
 #' Specialized [bbotk::CallbackAsync] for asynchronous tuning.
@@ -17,6 +17,26 @@ CallbackAsyncTuning = R6Class("CallbackAsyncTuning",
     #' Called in `ObjectiveTuningAsync$eval()`.
     on_eval_after_xs = NULL,
 
+    #' @field on_resample_begin (`function()`)\cr
+    #' Stage called at the beginning of an evaluation.
+    #' Called in `workhorse()` (internal).
+    on_resample_begin = NULL,
+
+    #' @field on_resample_before_train (`function()`)\cr
+    #' Stage called before training the learner.
+    #' Called in `workhorse()` (internal).
+    on_resample_before_train = NULL,
+
+    #' @field on_resample_before_predict (`function()`)\cr
+    #' Stage called before predicting.
+    #' Called in `workhorse()` (internal).
+    on_resample_before_predict = NULL,
+
+    #' @field on_resample_end (`function()`)\cr
+    #' Stage called at the end of an evaluation.
+    #' Called in `workhorse()` (internal).
+    on_resample_end = NULL,
+
     #' @field on_eval_after_resample (`function()`)\cr
     #' Stage called after hyperparameter configurations are evaluated.
     #' Called in `ObjectiveTuningAsync$eval()`.
@@ -52,6 +72,12 @@ CallbackAsyncTuning = R6Class("CallbackAsyncTuning",
 #'            - on_optimizer_before_eval
 #'              Start Evaluation
 #'                - on_eval_after_xs
+#'                  Start Resampling Iteration
+#'                    - on_resample_begin
+#'                    - on_resample_before_train
+#'                    - on_resample_before_predict
+#'                    - on_resample_end
+#'                  End Resampling Iteration
 #'                - on_eval_after_resample
 #'                - on_eval_before_archive
 #'              End Evaluation
@@ -72,7 +98,7 @@ CallbackAsyncTuning = R6Class("CallbackAsyncTuning",
 #' @details
 #' When implementing a callback, each function must have two arguments named `callback` and `context`.
 #' A callback can write data to the state (`$state`), e.g. settings that affect the callback itself.
-#' Tuning callbacks access [ContextAsyncTuning].
+#' Tuning callbacks access [ContextAsyncTuning] and [mlr3::ContextResample].
 #'
 #' @param id (`character(1)`)\cr
 #'  Identifier for the new instance.
@@ -101,6 +127,26 @@ CallbackAsyncTuning = R6Class("CallbackAsyncTuning",
 #'  Called in `ObjectiveTuningAsync$eval()`.
 #'  The functions must have two arguments named `callback` and `context`.
 #'  The argument of `$.eval(xs)` is available in the `context`.
+#' @param on_resample_begin (`function()`)\cr
+#'  Stage called at the beginning of a resampling iteration.
+#'  Called in `workhorse()` (internal).
+#'  See also [mlr3::callback_resample()].
+#'  The functions must have two arguments named `callback` and `context`.
+#' @param on_resample_before_train (`function()`)\cr
+#'  Stage called before training the learner.
+#'  Called in `workhorse()` (internal).
+#'  See also [mlr3::callback_resample()].
+#'  The functions must have two arguments named `callback` and `context`.
+#' @param on_resample_before_predict (`function()`)\cr
+#'  Stage called before predicting.
+#'  Called in `workhorse()` (internal).
+#'  See also [mlr3::callback_resample()].
+#'  The functions must have two arguments named `callback` and `context`.
+#' @param on_resample_end (`function()`)\cr
+#'  Stage called at the end of a resampling iteration.
+#'  Called in `workhorse()` (internal).
+#'  See also [mlr3::callback_resample()].
+#'  The functions must have two arguments named `callback` and `context`.
 #' @param on_eval_after_resample (`function()`)\cr
 #'  Stage called after a hyperparameter configuration is evaluated.
 #'  Called in `ObjectiveTuningAsync$eval()`.
@@ -152,6 +198,10 @@ callback_async_tuning = function(
   on_worker_begin = NULL,
   on_optimizer_before_eval = NULL,
   on_eval_after_xs = NULL,
+  on_resample_begin = NULL,
+  on_resample_before_train = NULL,
+  on_resample_before_predict = NULL,
+  on_resample_end = NULL,
   on_eval_after_resample = NULL,
   on_eval_before_archive = NULL,
   on_optimizer_after_eval = NULL,
@@ -167,6 +217,10 @@ callback_async_tuning = function(
     on_worker_begin,
     on_optimizer_before_eval,
     on_eval_after_xs,
+    on_resample_begin,
+    on_resample_before_train,
+    on_resample_before_predict,
+    on_resample_end,
     on_eval_after_resample,
     on_eval_before_archive,
     on_optimizer_after_eval,
@@ -181,6 +235,10 @@ callback_async_tuning = function(
       "on_worker_begin",
       "on_optimizer_before_eval",
       "on_eval_after_xs",
+      "on_resample_begin",
+      "on_resample_before_train",
+      "on_resample_before_predict",
+      "on_resample_end",
       "on_eval_after_resample",
       "on_eval_before_archive",
       "on_optimizer_after_eval",

R/CallbackBatchTuning.R

@@ -24,6 +24,26 @@ CallbackBatchTuning= R6Class("CallbackBatchTuning",
     #' Called in `ObjectiveTuningBatch$eval_many()`.
     on_eval_after_design = NULL,
 
+   #' @field on_resample_begin (`function()`)\cr
+    #' Stage called at the beginning of an evaluation.
+    #' Called in `workhorse()` (internal).
+    on_resample_begin = NULL,
+
+    #' @field on_resample_before_train (`function()`)\cr
+    #' Stage called before training the learner.
+    #' Called in `workhorse()` (internal).
+    on_resample_before_train = NULL,
+
+    #' @field on_resample_before_predict (`function()`)\cr
+    #' Stage called before predicting.
+    #' Called in `workhorse()` (internal).
+    on_resample_before_predict = NULL,
+
+    #' @field on_resample_end (`function()`)\cr
+    #' Stage called at the end of an evaluation.
+    #' Called in `workhorse()` (internal).
+    on_resample_end = NULL,
+
     #' @field on_eval_after_benchmark (`function()`)\cr
     #' Stage called after hyperparameter configurations are evaluated.
     #' Called in `ObjectiveTuningBatch$eval_many()`.
@@ -57,6 +77,12 @@ CallbackBatchTuning= R6Class("CallbackBatchTuning",
 #'          - on_optimizer_before_eval
 #'         Start Evaluation
 #'              - on_eval_after_design
+#'                  Start Resampling Iteration
+#'                    - on_resample_begin
+#'                    - on_resample_before_train
+#'                    - on_resample_before_predict
+#'                    - on_resample_end
+#'                  End Resampling Iteration
 #'              - on_eval_after_benchmark
 #'              - on_eval_before_archive
 #'         End Evaluation
@@ -70,7 +96,7 @@ CallbackBatchTuning= R6Class("CallbackBatchTuning",
 #' ```
 #'
 #' See also the section on parameters for more information on the stages.
-#' A tuning callback works with [ContextBatchTuning].
+#' A tuning callback works with [ContextBatchTuning] and [mlr3::ContextResample].
 #'
 #' @details
 #' When implementing a callback, each function must have two arguments named `callback` and `context`.
@@ -100,6 +126,26 @@ CallbackBatchTuning= R6Class("CallbackBatchTuning",
 #'  The functions must have two arguments named `callback` and `context`.
 #'  The arguments of `$eval_many(xss, resampling)` are available in `context`.
 #'  Additionally, the `design` is available in `context`.
+#' @param on_resample_begin (`function()`)\cr
+#'  Stage called at the beginning of a resampling iteration.
+#'  Called in `workhorse()` (internal).
+#'  See also [mlr3::callback_resample()].
+#'  The functions must have two arguments named `callback` and `context`.
+#' @param on_resample_before_train (`function()`)\cr
+#'  Stage called before training the learner.
+#'  Called in `workhorse()` (internal).
+#'  See also [mlr3::callback_resample()].
+#'  The functions must have two arguments named `callback` and `context`.
+#' @param on_resample_before_predict (`function()`)\cr
+#'  Stage called before predicting.
+#'  Called in `workhorse()` (internal).
+#'  See also [mlr3::callback_resample()].
+#'  The functions must have two arguments named `callback` and `context`.
+#' @param on_resample_end (`function()`)\cr
+#'  Stage called at the end of a resampling iteration.
+#'  Called in `workhorse()` (internal).
+#'  See also [mlr3::callback_resample()].
+#'  The functions must have two arguments named `callback` and `context`.
 #' @param on_eval_after_benchmark (`function()`)\cr
 #'  Stage called after hyperparameter configurations are evaluated.
 #'  Called in `ObjectiveTuningBatch$eval_many()`.
@@ -150,6 +196,10 @@ callback_batch_tuning = function(
   on_optimization_begin = NULL,
   on_optimizer_before_eval = NULL,
   on_eval_after_design = NULL,
+  on_resample_begin = NULL,
+  on_resample_before_train = NULL,
+  on_resample_before_predict = NULL,
+  on_resample_end = NULL,
   on_eval_after_benchmark = NULL,
   on_eval_before_archive = NULL,
   on_optimizer_after_eval = NULL,
@@ -163,6 +213,10 @@ callback_batch_tuning = function(
     on_optimization_begin,
     on_optimizer_before_eval,
     on_eval_after_design,
+    on_resample_begin,
+    on_resample_before_train,
+    on_resample_before_predict,
+    on_resample_end,
     on_eval_after_benchmark,
     on_eval_before_archive,
     on_optimizer_after_eval,
@@ -175,6 +229,10 @@ callback_batch_tuning = function(
       "on_optimization_begin",
       "on_optimizer_before_eval",
       "on_eval_after_design",
+      "on_resample_begin",
+      "on_resample_before_train",
+      "on_resample_before_predict",
+      "on_resample_end",
       "on_eval_after_benchmark",
       "on_eval_before_archive",
       "on_optimizer_after_eval",

R/ObjectiveTuningAsync.R

@@ -31,7 +31,7 @@ ObjectiveTuningAsync = R6Class("ObjectiveTuningAsync",
       lg$debug("Resampling hyperparameter configuration")
 
       # resample hyperparameter configuration
-      private$.resample_result = resample(self$task, self$learner, self$resampling, store_models = self$store_models, allow_hotstart = TRUE, clone = character(0))
+      private$.resample_result = resample(self$task, self$learner, self$resampling, store_models = self$store_models, allow_hotstart = TRUE, clone = character(0), callbacks = self$callbacks)
       call_back("on_eval_after_resample", self$callbacks, self$context)
 
       lg$debug("Aggregating performance")

R/ObjectiveTuningBatch.R

@@ -74,7 +74,8 @@ ObjectiveTuningBatch = R6Class("ObjectiveTuningBatch",
       private$.benchmark_result = benchmark(
         design = private$.design,
         store_models = self$store_models,
-        clone = character(0))
+        clone = character(0),
+        callbacks = self$callbacks)
       call_back("on_eval_after_benchmark", self$callbacks, self$context)
 
       # aggregate performance scores