fix: predict_newdata without coordinates (#102)

* fix: predict_newdata without coordinates

* ...

Author: be-marc

DESCRIPTION

@@ -42,6 +42,8 @@ Suggests:
     rpart,
     stars (>= 0.5-5),
     testthat (>= 3.0.0)
+Remotes:
+    mlr-org/mlr3
 VignetteBuilder:
     knitr
 Config/testthat/edition: 3

NAMESPACE

@@ -48,6 +48,7 @@ export(mask_stack)
 export(numeric_layer)
 export(predict_spatial)
 export(sample_stack)
+export(weights_layer)
 export(write_raster)
 import(checkmate)
 import(data.table)

R/data.R

@@ -43,6 +43,24 @@ factor_layer = function(id, levels, in_memory = FALSE) {
   list(id = id, type = "factor", levels = levels, in_memory = in_memory)
 }
 
+#' @title Weights Layer Generator
+#'
+#' @description
+#' Generates a weights layer when passed to [generate_stack()].
+#'
+#' @param id (`character(1)`)\cr
+#'   Layer id.
+#' @param in_memory (`logical(1)`)\cr
+#'   If `FALSE` (default), layer is written to disk.
+#'
+#' @keywords internal
+#' @export
+weights_layer = function(id, in_memory = FALSE) {
+  assert_string(id)
+  assert_flag(in_memory)
+  list(id = id, type = "weights", in_memory = in_memory)
+}
+
 #' @title Generate Raster Stack
 #'
 #' @description
@@ -96,6 +114,15 @@ generate_stack = function(layers, layer_size = NULL, dimension = NULL, multi_lay
         ras = rast(filename)
       }
       ras
+    } else if (layer$type == "weights") {
+      data = matrix(runif(dimension^2, 0, 1), nrow = dimension)
+      ras = rast(data)
+      if (!layer$in_memory && !multi_layer_file) {
+        filename = tempfile(fileext = ".tif")
+        writeRaster(ras, filename)
+        ras = rast(filename)
+      }
+      ras
     }
   })
 

R/zzz.R

@@ -71,6 +71,9 @@
   x$default_measures$classif_st = "classif.ce"
   x$default_measures$regr_st = "regr.mse"
 
+  x$task_col_roles_optional_newdata$classif_st = c(x$task_col_roles_optional_newdata$classif, c("coordinate", "space", "time"))
+  x$task_col_roles_optional_newdata$regr_st = c(x$task_col_roles_optional_newdata$regr, c("coordinate", "space", "time"))
+
   # task
   x = getFromNamespace("mlr_tasks", ns = "mlr3")
   x$add("leipzig", load_task_leipzig)

man/TaskClassifST.Rd

@@ -0,0 +1 @@
+lapply(list.files(system.file("testthat", package = "mlr3"), pattern = "^helper.*\\.[rR]", full.names = TRUE), source)

man/TaskRegrST.Rd

@@ -22,3 +22,23 @@ test_that("LearnerClassifSpatial ignores observations with missing values", {
   expect_true(all(is.na(pred$response[seq(100)])))
   expect_numeric(pred$response, any.missing = TRUE, all.missing = FALSE)
 })
+
+test_that("LearnerClassifSpatial predicts newdata without optional column roles", {
+  stack = generate_stack(list(
+    numeric_layer("x_1"),
+    weights_layer("weights"),
+    factor_layer("y", levels = c("a", "b"))),
+  dimension = 100)
+  vector = sample_stack(stack, n = 100)
+  task_train = as_task_classif_st(vector, id = "test_vector", target = "y")
+  task_train$set_col_roles("weights", roles = "weights_learner")
+
+  learner = lrn("classif.rpart")
+  learner$train(task_train)
+
+  stack$weights = NULL
+  task_predict = as_task_unsupervised(stack, id = "test")
+  pred = learner$predict_newdata(task_predict$data())
+
+  expect_prediction(pred)
+})