Reworked outdated functions and methods.

DESCRIPTION

@@ -204,8 +204,11 @@ Collate:
     'TaskVimp.R'
     'TaskVimpHyperparameters.R'
     'TestDataCreators.R'
+    'TestFeatureInfo.R'
     'TestFunctions.R'
-    'TrainS4Methods.R'
+    'TestTrain.R'
+    'TestTrainNovelty.R'
+    'TestVimp.R'
     'TrimUtilities.R'
     'Utilities.R'
     'UtilitiesS4.R'

R/FamiliarS4Generics.R

@@ -234,7 +234,7 @@ setGeneric(".vimp", function(object, ...) standardGeneric(".vimp"))
 
 setGeneric("promote_vimp_method", function(object, ...) standardGeneric("promote_vimp_method"))
 
-setGeneric("prepare_vimp_object", function(data, ...) standardGeneric("prepare_vimp_object"))
+setGeneric("test_create_vimp_method", function(data, ...) standardGeneric("test_create_vimp_method"))
 
 
 

R/FeatureInfo.R

@@ -2,62 +2,6 @@
 #' @include FamiliarS4Classes.R
 NULL
 
-create_feature_info <- function(
-    data,
-    signature = NULL,
-    ...
-) {
-  # This creates a list of featureInfo objects, with processing, based on data.
-  # This code is primarily used within unit tests.
-
-  # Reconstitute settings from the data.
-  settings <- extract_settings_from_data(data = data)
-
-  # Update some missing settings that can be fixed within this method.
-  settings$data$train_cohorts <- unique(data@data[[get_id_columns(single_column = "batch")]])
-
-  # Parse the remaining settings that are important.
-  settings <- do.call(
-    .parse_general_settings,
-    args = c(
-      list(
-        "settings" = settings,
-        "data" = data@data
-      ),
-      list(...)
-    )
-  )
-
-  # Create a list of featureInfo objects.
-  feature_info_list <- .get_feature_info_data(
-    data = data@data,
-    file_paths = NULL,
-    project_id = character(),
-    outcome_type = data@outcome_type
-  )
-
-  # Extract the generic data.
-  feature_info_list <- feature_info_list[["generic"]]
-
-  # Add signature feature info
-  feature_info_list <- add_signature_info(
-    feature_info_list = feature_info_list, 
-    signature = signature
-  )
-
-  # Perform some pre-processing (i.e. remove singular features)
-  feature_info_list <- .determine_preprocessing_parameters(
-    cl = NULL,
-    data = data,
-    feature_info_list = feature_info_list,
-    settings = settings,
-    verbose = FALSE
-  )
-
-  return(feature_info_list)
-}
-
-
 
 .get_feature_info_data <- function(
     data,

R/ParseData.R

@@ -804,17 +804,19 @@
     data_column_info = create_data_column_info(settings = settings)
   )
 
+  # Add generic feature information.
+  generic_feature_info_task <- new("familiarTaskGenericFeatureInfo")
+  generic_feature_info <- .perform_task(
+    object = generic_feature_info_task,
+    data = data
+  )
+
   # Set up vimp object and promote to concordance.
   vimp_object <- promote_vimp_method(methods::new(
     "familiarVimpMethod",
     outcome_type = data@outcome_type,
     outcome_info = data@outcome_info,
-    feature_info = .get_feature_info_data(
-      data = data@data,
-      file_paths = NULL,
-      project_id = character(),
-      outcome_type = settings$data$outcome_type
-    )[["generic"]],
+    feature_info = generic_feature_info,
     vimp_method = "concordance"
   ))
 

R/TestFeatureInfo.R

@@ -0,0 +1,59 @@
+test_create_generic_info <- function(
+    data
+) {
+  # Setup feature info task.
+  feature_info_task <- methods::new(
+    "familiarTaskGenericFeatureInfo"
+  )
+
+  # Feature information objects are created from the bypass dataset.
+  feature_info <- .perform_task(
+    object = feature_info_task,
+    data = data
+  )
+
+  return(feature_info)
+}
+
+
+
+test_create_feature_info <- function(
+    data,
+    signature = NULL,
+    ...
+) {
+  # This creates a list of featureInfo objects, with processing, based on data.
+  # This code is primarily used within unit tests.
+
+  # Reconstitute settings from the data.
+  settings <- extract_settings_from_data(data = data)
+
+  # Update some missing settings that can be fixed within this method.
+  settings$data$train_cohorts <- unique(data@data[[get_id_columns(single_column = "batch")]])
+
+  # Parse the remaining settings that are important.
+  settings <- do.call(
+    .parse_general_settings,
+    args = c(
+      list(
+        "settings" = settings,
+        "data" = data@data
+      ),
+      list(...)
+    )
+  )
+
+  # Setup feature info task.
+  feature_info_task <- methods::new(
+    "familiarTaskFeatureInfo"
+  )
+
+  # Feature information objects are created from the bypass dataset.
+  feature_info <- .perform_task(
+    object = feature_info_task,
+    data = data,
+    settings = settings
+  )
+
+  return(feature_info)
+}

R/TestFunctions.R

@@ -2139,7 +2139,7 @@ test_all_vimp_methods <- function(
       # Full dataset -----------------------------------------------------------
 
       # Process dataset.
-      vimp_object <- prepare_vimp_object(
+      vimp_object <- test_create_vimp_method(
         data = full_data,
         vimp_method = vimp_method,
         vimp_method_parameter_list = hyperparameters,
@@ -2250,7 +2250,7 @@ test_all_vimp_methods <- function(
       # One-feature dataset ----------------------------------------------------
 
       # Process dataset.
-      vimp_object <- prepare_vimp_object(
+      vimp_object <- test_create_vimp_method(
         data = one_feature_data,
         vimp_method = vimp_method,
         vimp_method_parameter_list = hyperparameters,
@@ -2317,7 +2317,7 @@ test_all_vimp_methods <- function(
         no_censoring_data <- test_create_good_data_without_censoring(outcome_type)
 
         # Process dataset.
-        vimp_object <- prepare_vimp_object(
+        vimp_object <- test_create_vimp_method(
           data = no_censoring_data,
           vimp_method = vimp_method,
           vimp_method_parameter_list = hyperparameters,
@@ -2354,7 +2354,7 @@ test_all_vimp_methods <- function(
         one_censored_data <- test_create_good_data_one_censored(outcome_type)
 
         # Process dataset.
-        vimp_object <- prepare_vimp_object(
+        vimp_object <- test_create_vimp_method(
           data = one_censored_data,
           vimp_method = vimp_method,
           vimp_method_parameter_list = hyperparameters,
@@ -2390,7 +2390,7 @@ test_all_vimp_methods <- function(
         few_censored_data <- test_create_good_data_few_censored(outcome_type)
 
         # Process dataset.
-        vimp_object <- prepare_vimp_object(
+        vimp_object <- test_create_vimp_method(
           data = few_censored_data,
           vimp_method = vimp_method,
           vimp_method_parameter_list = hyperparameters,
@@ -2426,7 +2426,7 @@ test_all_vimp_methods <- function(
       # Fully prospective dataset ----------------------------------------------
 
       # Set up the vimp object.
-      vimp_object <- prepare_vimp_object(
+      vimp_object <- test_create_vimp_method(
         data = full_data,
         vimp_method = vimp_method,
         vimp_method_parameter_list = hyperparameters,
@@ -2553,7 +2553,7 @@ test_all_vimp_methods_parallel <- function(
           "1" = full_data, 
           "2" = full_data
         ),
-        prepare_vimp_object,
+        test_create_vimp_method,
         vimp_method = vimp_method,
         vimp_method_parameter_list = hyperparameters,
         outcome_type = outcome_type,
@@ -5253,7 +5253,7 @@ test_not_deprecated <- function(x, deprecation_string = c("deprec", "replac")) {
   }
 
   # Create feature info list.
-  feature_info_list <- create_feature_info(
+  feature_info_list <- test_create_feature_info(
     data = data,
     vimp_method = vimp_method,
     learner = learner,