This package adds resampling methods for the {mlr3} package framework suited for spatial, temporal and spatiotemporal data. These methods can help to reduce the influence of autocorrelation on performance estimates when performing cross-validation. While this article gives a rather technical introduction to the package, a more applied approach can be found in the mlr3book section on “Spatiotemporal Analysis”.
After loading the package via library("mlr3spatiotempcv"), the spatiotemporal resampling methods and example tasks provided by {mlr3spatiotempcv} are available to the user alongside the default {mlr3} resampling methods and tasks.
To make use of spatial resampling methods, a {mlr3} task that is aware of its spatial characteristic needs to be created. Two child classes exist in {mlr3spatiotempcv} for this purpose:
TaskClassifSTTaskRegrSTTo create one of these, one can either pass a sf object as the “backend” directly:
# create 'sf' object
data_sf = sf::st_as_sf(ecuador, coords = c("x", "y"), crs = 4326)
# create mlr3 task
task = TaskClassifST$new("ecuador_sf",
backend = data_sf, target = "slides", positive = "TRUE"
)or use a plain data.frame. In this case, the constructor of TaskClassifST needs a few more arguments:
data = mlr3::as_data_backend(ecuador)
task = TaskClassifST$new("ecuador",
backend = data, target = "slides",
positive = "TRUE", extra_args = list(coordinate_names = c("x", "y"))
)Now this Task can be used as a normal {mlr3} task in any kind of modeling scenario. Have a look at the mlr3book section on “Spatiotemporal Analysis” on how to apply a spatiotemporal resampling method to such a task.
In {mlr3}, dictionaries are used for overview purposes of available methods. The following sections show which dictionaries get appended with new entries when loading {mlr3spatiotempcv}.
Additional task types:
TaskClassifST
TaskRegrST
mlr_reflections$task_types
#> type package task learner prediction
#> 1: classif mlr3 TaskClassif LearnerClassif PredictionClassif
#> 2: classif mlr3spatiotempcv TaskClassifST LearnerClassif PredictionClassif
#> 3: regr mlr3 TaskRegr LearnerRegr PredictionRegr
#> 4: regr mlr3spatiotempcv TaskRegrST LearnerRegr PredictionRegr
#> measure
#> 1: MeasureClassif
#> 2: MeasureClassif
#> 3: MeasureRegr
#> 4: MeasureRegrAdditional column roles:
coordinatesmlr_reflections$task_col_roles
#> $regr
#> [1] "feature" "target" "name" "order" "stratum" "group" "weight"
#> [8] "uri"
#>
#> $classif
#> [1] "feature" "target" "name" "order" "stratum" "group" "weight"
#> [8] "uri"
#>
#> $classif_st
#> [1] "feature" "target" "name" "order" "stratum"
#> [6] "group" "weight" "uri" "coordinates"
#>
#> $regr_st
#> [1] "feature" "target" "name" "order" "stratum"
#> [6] "group" "weight" "uri" "coordinates"Additional resampling methods:
spcv_block
spcv_buffer
spcv_coords
spcv_env
sptcv_cluto
sptcv_cstf
and their respective repeated versions.
as.data.table(mlr_resamplings)
#> key params iters
#> 1: bootstrap repeats,ratio 30
#> 2: custom 0
#> 3: cv folds 10
#> 4: holdout ratio 1
#> 5: insample 1
#> 6: loo NA
#> 7: repeated_cv repeats,folds 100
#> 8: repeated_spcv_block folds,repeats,rows,cols,range,selection 10
#> 9: repeated_spcv_coords folds,repeats 10
#> 10: repeated_spcv_env folds,repeats,features 10
#> 11: repeated_sptcv_cluto folds,repeats 10
#> 12: repeated_sptcv_cstf folds,repeats 10
#> 13: spcv_block folds,rows,cols,range,selection 10
#> 14: spcv_buffer theRange,spDataType,addBG 0
#> 15: spcv_coords folds 10
#> 16: spcv_env folds,features 10
#> 17: sptcv_cluto folds 10
#> 18: sptcv_cstf folds 10
#> 19: subsampling repeats,ratio 30Additional example tasks:
tsk("ecuador") (spatial, classif)tsk("cookfarm") (spatiotemp, regr)The following table lists all methods implemented in {mlr3spatiotempcv}, their upstream R package and scientific references.
| Literature | Package | Reference | mlr3 Sugar |
|---|---|---|---|
| Spatial Buffering | blockCV | Valavi et al. (2018) | rsmp("spcv_buffer") |
| Spatial Blocking | blockCV | Valavi et al. (2018) | rsmp("spcv_block") |
| Spatial CV | sperrorest | Brenning (2012) | rsmp("spcv_coords") |
| Environmental Blocking | blockCV | Valavi et al. (2018) | rsmp("spcv_env") |
| - | - | - | rsmp("sptcv_cluto") |
| Leave-Location-and-Time-Out | CAST | Meyer et al. (2018) | rsmp("sptcv_cstf") |
| Spatiotemporal Clustering | skmeans | Zhao and Karypis (2002) | rsmp("repeated_sptcv_cluto") |
| Repeated Spatial Blocking | blockCV | Valavi et al. (2018) | rsmp("repeated_spcv_block") |
| Repeated Spatial CV | sperrorest | Brenning (2012) | rsmp("repeated_spcv_coords") |
| Repeated Env Blocking | blockCV | Valavi et al. (2018) | rsmp("repeated_spcv_env") |
| - | - | - | rsmp("repeated_sptcv_cluto") |
| Repeated Leave-Location-and-Time-Out | CAST | Meyer et al. (2018) | | rsmp("repeated_sptcv_cstf") |
| Repeated Spatiotemporal Clustering | skmeans | Zhao and Karypis (2002) | rsmp("repeated_sptcv_cluto") |