mlr_resamplings_spcv_env | R Documentation |
Splits data by clustering in the feature space.
See the upstream implementation at blockCV::cv_cluster()
and
Valavi et al. (2018) for further information.
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.
folds
(integer(1)
)
Number of folds.
features
(character()
)
The features to use for clustering.
mlr3::Resampling
-> ResamplingSpCVEnv
iters
integer(1)
Returns the number of resampling iterations, depending on the
values stored in the param_set
.
new()
Create an "Environmental Block" resampling instance.
For a list of available arguments, please see blockCV::cv_cluster.
ResamplingSpCVEnv$new(id = "spcv_env")
id
character(1)
Identifier for the resampling strategy.
instantiate()
Materializes fixed training and test splits for a given task.
ResamplingSpCVEnv$instantiate(task)
task
Task
A task to instantiate.
clone()
The objects of this class are cloneable with this method.
ResamplingSpCVEnv$clone(deep = FALSE)
deep
Whether to make a deep clone.
Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G (2018). “blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models.” bioRxiv. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1101/357798")}.
if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
library(mlr3)
task = tsk("ecuador")
# Instantiate Resampling
rcv = rsmp("spcv_env", folds = 4)
rcv$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
}
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