Merge pull request #1388 from M3nin0/fix/accuracy-empty-points

enhance sits_accuracy message in case of empty results

Author: gilbertocamara

R/api_accuracy.R

@@ -194,6 +194,9 @@
     .check_chr_contains(colnames(validation), "label")
     # transform the `sf` object in a valid
     validation |>
+        sf::st_transform(
+            crs = "EPSG:4326"
+        ) |>
         dplyr::mutate(
             geom = sf::st_geometry(validation)
         ) |>

R/sits_accuracy.R

@@ -212,7 +212,6 @@ sits_accuracy.class_cube <- function(data, ...,
         if (nrow(points_tile) < 1L) {
             return(NULL)
         }
-
         # convert the tibble to a matrix
         xy <- matrix(c(points_tile[["X"]], points_tile[["Y"]]),
             nrow = nrow(points_tile),
@@ -243,6 +242,9 @@ sits_accuracy.class_cube <- function(data, ...,
             reference = reference
         )
     })
+    if (.has_not(.dissolve(pred_ref_lst))) {
+        stop(.conf("messages", "sits_accuracy_class_cube_no_validation_points"))
+    }
     # retrieve predicted and reference vectors for all rows of the cube
     pred_ref <- do.call(rbind, pred_ref_lst)
     # is this data valid?

inst/extdata/config_messages.yml

@@ -360,6 +360,7 @@ sits_add_base_cube: "unable to add cube - check if cube2 contains base maps"
 sits_accuracy: "unable to extract values to measure accuracy - check input"
 sits_accuracy_sits: "unable to extract predicted and reference data from samples\n have the samples been classified?"
 sits_accuracy_class_cube: "invalid validation data"
+sits_accuracy_class_cube_no_validation_points: "No validation points were extracted from the classified data cube - ensure they intersect"
 sits_accuracy_raster_cube: "input should be a classified cube"
 sits_accuracy_class_vector_cube: "attributes for predicted and reference values should be contained in the segments object"
 sits_accuracy_tbl_df: "input should be a classified sits tibble or a classified data cube"

tests/testthat/test-accuracy.R

@@ -1,4 +1,4 @@
-test_that("conf_matrix -2 classes", {
+test_that("conf_matrix - 2 classes", {
     data(cerrado_2classes)
     set.seed(1234)
     train_data <- sits_sample(cerrado_2classes, frac = 0.5)