WIP on feature selection-less training.

R/Familiar.R

@@ -376,6 +376,7 @@ summon_familiar <- function(
   if (.stop_after == "training") {
     tasks <- .generate_trainer_tasks(
       experiment_data = experiment_data,
+      optimisation_determine_vimp = settings$hpo$hpo_determine_vimp,
       vimp_methods = settings$vimp$vimp_methods,
       learners = settings$mb$learners,
       file_paths = file_paths
@@ -442,10 +443,13 @@ summon_familiar <- function(
 
   # Check if the process should be stopped at this point.
   if (.stop_after %in% c("vimp")) {
-    feature_info <- get_feature_info_from_backend(
-      data_id = waiver(),
-      run_id = waiver()
-    )
+    feature_info <- NULL
+    if (!is_empty(tasks$feature_info)) {
+      feature_info <- get_feature_info_from_backend(
+        data_id = waiver(),
+        run_id = waiver()
+      )
+    }
 
     vimp_hyperparameters <- NULL
     if (!is_empty(tasks$hyperparameters_vimp)) {

R/HyperparameterOptimisation.R

@@ -1410,7 +1410,7 @@ setMethod(
     )
 
     # Attach variable importance tables.
-    object@vimp_table <- decluster_vimp_table(vimp_table_list)
+    object@vimp_table <- vimp_table_list
 
     return(object)
   }

R/HyperparameterOptimisationUtilities.R

@@ -445,10 +445,7 @@
     feature_info_list = feature_info,
     data = data
   )
-
-  # Form clusters.
-  vimp_table <- recluster_vimp_table(vimp_table)
-
+
   return(vimp_table)
 }
 

R/TaskFeatureInfo.R

@@ -489,18 +489,26 @@ setMethod(
   data_id <- run_id <- list_name <- complete <- NULL
 
   # Create or load generic feature info.
-  if (!.file_exists(tasks$generic_feature_info[[1L]])) {
-    generic_feature_info <- .perform_task(
-      object = tasks$generic_feature_info[[1L]],
-      data = NULL,
-      experiment_data = experiment_data,
-      ...
-    )
+  if (!is_empty(tasks$generic_feature_info)) {
+    if (!.file_exists(tasks$generic_feature_info[[1L]])) {
+      generic_feature_info <- .perform_task(
+        object = tasks$generic_feature_info[[1L]],
+        data = NULL,
+        experiment_data = experiment_data,
+        ...
+      )
+
+    } else {
+      generic_feature_info <- readRDS(tasks$generic_feature_info[[1L]]@file)
+    }
 
   } else {
-    generic_feature_info <- readRDS(tasks$generic_feature_info[[1L]]@file)
+    generic_feature_info <- NULL
   }
 
+  # Check that any feature info tasks are required.
+  if (is_empty(tasks$feature_info)) return(invisible(FALSE))
+
   # Determine which feature info objects need to be obtained.
   finished_tasks <- sapply(tasks$feature_info, .file_exists)
   unfinished_tasks <- tasks$feature_info[!finished_tasks]
@@ -510,7 +518,7 @@ setMethod(
   if (length(unfinished_tasks) > 0L) {
     ..run_preprocessing(
       tasks = unfinished_tasks,
-      generic_feature_info <- generic_feature_info,
+      generic_feature_info = generic_feature_info,
       experiment_data = experiment_data,
       ...
     )

R/TaskLearn.R

@@ -416,13 +416,14 @@ setMethod(
 
 .generate_trainer_tasks <- function(
     experiment_data,
+    optimisation_determine_vimp,
     vimp_methods,
     learners,
     file_paths,
     skip_existing = FALSE
 ) {
   # Suppress NOTES due to non-standard evaluation in data.table
-  train <- main_data_id <- can_pre_process <- NULL
+  train <- main_data_id <- can_pre_process <- vimp <- NULL
 
   # Find the data_id related to model training.
   data_id <- experiment_data@experiment_setup[train == TRUE, ]$main_data_id[1L]
@@ -474,6 +475,17 @@ setMethod(
 
   # learner hyperparameter tasks -----------------------------------------------
 
+  # Check how variable importance data should be handled.
+  if (is_empty(experiment_data@experiment_setup[vimp == TRUE, ])) {
+    use_vimp <- "return_hpo_vimp"
+
+  } else if (optimisation_determine_vimp) {
+    use_vimp <- "use_hpo_vimp"
+
+  } else {
+    use_vimp <- "use_main_vimp"
+  }
+
   # Set up variable importance hyperparameter task.
   train_run_table <- .get_run_table_from_experiment_setup(
     data_id = data_id,
@@ -495,6 +507,7 @@ setMethod(
           "familiarTaskLearnerHyperparameters",
           data_id = learner_hyperparameter_data_id,
           run_id = run_id,
+          use_vimp = use_vimp,
           vimp_method = vimp_method,
           learner = learner,
           run_table = run_tables,

R/TaskVimp.R

@@ -407,7 +407,7 @@ setMethod(
 
   # Find the data_id related to computing variable importance.
   data_id <- experiment_data@experiment_setup[vimp == TRUE, ]$main_data_id[1L]
-  if (is_empty(data_id)) return(NULL)
+  if (is.na(data_id)) return(NULL)
 
   # Initialise empty list.
   task_list <- list()

R/VimpTable.R

@@ -372,7 +372,7 @@ setMethod(
   function(x, ...) {
     # If the list is empty, return NULL instead.
     if (is_empty(x)) return(NULL)
-
+    browser()
     # Dispatch to method for single variable importance tables.
     return(lapply(
       x,
@@ -388,7 +388,7 @@ setMethod(
   "decluster_vimp_table",
   signature(x = "vimpTable"),
   function(x, show_weights = FALSE, show_cluster_name = FALSE, ...) {
-    browser()
+
     # Check if the table has already been declustered.
     if (.as_vimp_table_state(x@state) >= "declustered") return(x)
 

tests/testthat/test-task_based_workflow.R

@@ -66,3 +66,41 @@ model <- familiar::train_familiar(
   verbose = TRUE,
   parallel = FALSE
 )
+
+
+# Check without explicit variable importance computation -----------------------
+# Create variable importance
+experiment_vimp <- familiar::precompute_vimp(
+  data = data,
+  experimental_design = "bs(mb,3)",
+  vimp_method = "mim",
+  outcome_type = "binomial",
+  outcome_column = "outcome",
+  batch_id_column = "batch_id",
+  sample_id_column = "sample_id",
+  series_id_column = "series_id",
+  class_levels = c("red", "green"),
+  verbose = TRUE,
+  parallel = FALSE
+)
+
+testthat::test_that("variable importance data is absent", {
+  testthat::expect_null(experiment_vimp@feature_info)
+  testthat::expect_null(experiment_vimp@vimp_table_list)
+})
+
+# Train model
+model <- familiar::train_familiar(
+  data = data,
+  experiment_data = experiment_vimp,
+  vimp_method = "mim",
+  learner = "glm_logistic",
+  outcome_type = "binomial",
+  outcome_column = "outcome",
+  batch_id_column = "batch_id",
+  sample_id_column = "sample_id",
+  series_id_column = "series_id",
+  class_levels = c("red", "green"),
+  verbose = TRUE,
+  parallel = FALSE
+)