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(blockCV) "Environmental blocking" resampling

Source: R/ResamplingSpCVEnv.R
mlr_resamplings_spcv_env.Rd

Splits data by clustering in the feature space. See the upstream implementation at blockCV::cv_cluster() and Valavi et al. (2018) for further information.

Details

Useful when the dataset is supposed to be split on environmental information which is present in features. The method allows for a combination of multiple features for clustering.

The input of raster images directly as in blockCV::cv_cluster() is not supported. See mlr3spatial and its raster DataBackends for such support in mlr3.

Parameters

  • folds (integer(1))
    Number of folds.

  • features (character())
    The features to use for clustering.

References

Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G (2018). “blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models.” bioRxiv. doi:10.1101/357798 .

Super class

mlr3::Resampling -> ResamplingSpCVEnv

Active bindings

iters

integer(1)
Returns the number of resampling iterations, depending on the values stored in the param_set.

Methods

Public methods

Inherited methods

Method new()

Create an "Environmental Block" resampling instance.

For a list of available arguments, please see blockCV::cv_cluster.

Usage

ResamplingSpCVEnv$new(id = "spcv_env")

Arguments

id

character(1)
Identifier for the resampling strategy.

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSpCVEnv$instantiate(task)

Arguments

task

Task
A task to instantiate.

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSpCVEnv$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# \donttest{
if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  library(mlr3)
  task = tsk("ecuador")

  # Instantiate Resampling
  rcv = rsmp("spcv_env", folds = 4)
  rcv$instantiate(task)

  # Individual sets:
  rcv$train_set(1)
  rcv$test_set(1)
  intersect(rcv$train_set(1), rcv$test_set(1))

  # Internal storage:
  rcv$instance
}
#> Key: <fold>
#>      row_id  fold
#>       <int> <int>
#>   1:    682     1
#>   2:    464     2
#>   3:      3     3
#>   4:    135     3
#>   5:    192     3
#>  ---             
#> 747:    747     4
#> 748:    748     4
#> 749:    749     4
#> 750:    750     4
#> 751:    751     4
# }