fix subset covariates to support out of order covariates. Covariates …

R/Lrnr_base.R

@@ -56,6 +56,13 @@ Lrnr_base <- R6Class(
         if (length(delta_idx) > 0) {
           delta_missing <- task_covs_missing[delta_idx]
           task_covs_missing <- task_covs_missing[-delta_idx]
+
+          delta_missing_data <- matrix(0, nrow(task$data), length(delta_idx))
+          colnames(delta_missing_data) <- delta_missing
+          cols <- task$add_columns(data.table(delta_missing_data))
+
+        } else{
+          cols <- task$column_names
         }
 
         # error when task is missing covariates
@@ -68,29 +75,10 @@ Lrnr_base <- R6Class(
           )
         }
 
-        # subset task covariates to only includes those in learner covariates
-        covs_subset <- intersect(task_covs, learner_covs)
-
-        # return updated task
-        if (length(delta_idx) == 0) {
-          # re-order the covariate subset to match order of learner covariates
-          ordered_covs_subset <- covs_subset[match(covs_subset, learner_covs)]
-          return(task$next_in_chain(covariates = ordered_covs_subset))
-        } else {
-          # incorporate missingness indicators in task covariates subset & sort
-          covs_subset_delta <- c(covs_subset, delta_missing)
-          ord_covs <- covs_subset_delta[match(covs_subset_delta, learner_covs)]
-
-          # incorporate missingness indicators in task data
-          delta_missing_data <- matrix(0, nrow(task$data), length(delta_idx))
-          colnames(delta_missing_data) <- delta_missing
-          cols <- task$add_columns(data.table(delta_missing_data))
-
-          return(task$next_in_chain(
-            covariates = ord_covs,
+        return(task$next_in_chain(
+            covariates = learner_covs,
             column_names = cols
-          ))
-        }
+        ))
       } else {
         return(task)
       }

R/utils.R

@@ -105,10 +105,12 @@ call_with_args <- function(fun, args, other_valid = list(), keep_all = FALSE,
     # subset arguments to pass
     args <- args[which(names(args) %in% all_valid)]
 
+    # don't warn on covariate param
+    invalid <- setdiff(invalid, "covariates")
     # return warnings when dropping arguments
     if (!silent & length(invalid) > 0) {
       message(sprintf(
-        "Learner called function %s with unknown args: %s. These will be dropped.\nCheck the params supported by this learner.",
+        "Learner called function %s with unknown args: %s. These will be dropped.\nCheck the params supported by this learner.\n",
         as.character(substitute(fun)), paste(invalid, collapse = ", ")
       ))
     }

tests/testthat/test-subset_covariates.R

@@ -31,6 +31,14 @@ full_preds <- glm_fit_pre_subset$predict(task)
 training_preds <- glm_fit_pre_subset$predict()
 test_that("extra covariates in prediction set get dropped correctly", expect_equal(full_preds, training_preds))
 
+
+shuffled_subset <- sample(covariate_subset)
+task_pre_subset_shuffled <- sl3_Task$new(mtcars, covariates = shuffled_subset, outcome = outcome)
+# debugonce(glm_fit_pre_subset$subset_covariates)
+shuffled_preds <- glm_fit_pre_subset$predict(task_pre_subset_shuffled)
+test_that("covariates out of order prediction set get shuffled correctly", expect_equal(full_preds, shuffled_preds))
+
+
 task_train <- sl3_Task$new(mtcars, covariates = covariates, outcome = outcome)
 task_predict <- sl3_Task$new(mtcars, covariates = covariate_subset, outcome = outcome)
 glm_fit <- lrnr_glm$train(task_train)
@@ -47,11 +55,10 @@ task_missing_data <- suppressWarnings(
   sl3_Task$new(missing_data, covariates = covs, outcome = Y)
 )
 
-lrnr_glm <- make_learner(Lrnr_glm_fast, name = "test")
+lrnr_glm <- make_learner(Lrnr_glm_fast)
 glm_fit <- lrnr_glm$train(task_missing_data)
 
 task_complete_data <- sl3_Task$new(mtcars, covariates = covs, outcome = Y)
-
 test_that("missingness indicators in prediction task works", {
   expect_vector(glm_fit$predict(task_complete_data))
 })