With prediction_sites as a single polygon, spatial_nndm_cv() produces a LOOCV

[julienvollering]

The problem

Related to #145
I think there's a bug when passing a polygon as prediction_sites to spatial_nndm_cv. When I use a single single sfc_POLYGON, the assessment folds do not have any buffer around them -- the CV just becomes a LOOCV. When I pass a sfc_POINT object sampled from the same sfc_POLYGON, the NNDM has folds as expected.

Wrapping the sfc_POLYGON in a call to st_sample is of course a very easy workaround, but the documentation clearly states the desired behavior for a single polygon.

Having looked quickly at the source code, it seems to me that sample_points is not being carried forward as the prediction_sites (unless it lacks a CRS).

spatialsample/R/spatial_nndm_cv.R

Line 131 in 9877514

sample_points <- sf::st_sample(

Reproducible example

library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE
library(spatialsample)
data(ames, package = "modeldata")

ames_sf <- st_as_sf(ames, coords = c("Longitude", "Latitude"), crs = 4326)

# Passing prediction_sites as single sfc_POLYGON
ch <- st_concave_hull(st_union(ames_sf), ratio = 0.4, allow_holes = TRUE)
str(ch)
#> sfc_POLYGON of length 1; first list element: List of 2
#>  $ : num [1:72, 1:2] -93.7 -93.7 -93.7 -93.7 -93.7 ...
#>  $ : num [1:6, 1:2] -93.6 -93.6 -93.6 -93.6 -93.6 ...
#>  - attr(*, "class")= chr [1:3] "XY" "POLYGON" "sfg"

set.seed(123)
nndm <- spatial_nndm_cv(ames_sf[1:100, ], ch) 
print(nndm, n=2)
#> # A tibble: 100 × 2
#>   splits         id     
#>   <list>         <chr>  
#> 1 <split [99/1]> Fold001
#> 2 <split [99/1]> Fold002
#> # ℹ 98 more rows
foldN <- nndm |> 
  rsample::tidy() |> 
  dplyr::count(Resample) |> 
  dplyr::pull(n)
all(foldN == 100)
#> [1] TRUE
autoplot(get_rsplit(nndm, 1))

# Passing prediction_sites as sfc_POINT
set.seed(123)
chpts <- st_sample(ch, size = 1000)
str(chpts)
#> sfc_POINT of length 1000; first list element:  'XY' num [1:2] -93.7 42

set.seed(123)
nndm2 <- spatial_nndm_cv(ames_sf[1:100, ], chpts) 
print(nndm2, n=2)
#> # A tibble: 100 × 2
#>   splits         id     
#>   <list>         <chr>  
#> 1 <split [50/1]> Fold001
#> 2 <split [50/1]> Fold002
#> # ℹ 98 more rows
autoplot(get_rsplit(nndm2, 1))

^{Created on 2024-08-27 with reprex v2.1.1}

Session info

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[mikemahoney218]

Yikes! Thanks for the report (and the excellent reprex); this will be fixed once I merge #161 into main.