mlr3spatiotempcv (version 0.1.1)

ResamplingRepeatedSpCVEnv: Repeated Environmental Block Cross Validation Resampling

Description

Environmental Block Cross Validation. This strategy uses k-means clustering to specify blocks of similar environmental conditions. Only numeric features can be used. The features used for building blocks can be specified in the param_set. By default, all numeric features are used.

Super class

mlr3::Resampling -> ResamplingRepeatedSpCVEnv

Active bindings

iters

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

Methods

Public methods

• ResamplingRepeatedSpCVEnv$new()

• ResamplingRepeatedSpCVEnv$folds()

• ResamplingRepeatedSpCVEnv$repeats()

• ResamplingRepeatedSpCVEnv$instantiate()

• ResamplingRepeatedSpCVEnv$clone()

• mlr3::Resampling$format()

• mlr3::Resampling$help()

• mlr3::Resampling$print()

• mlr3::Resampling$test_set()

• mlr3::Resampling$train_set()

Method new()

Create an "coordinate-based" repeated resampling instance.

Usage

ResamplingRepeatedSpCVEnv$new(id = "repeated_spcv_env")

Arguments

id

character(1) Identifier for the resampling strategy.

Method folds()

Translates iteration numbers to fold number.

Usage

ResamplingRepeatedSpCVEnv$folds(iters)

Arguments

iters

integer() Iteration number.

Method repeats()

Translates iteration numbers to repetition number.

Usage

ResamplingRepeatedSpCVEnv$repeats(iters)

Arguments

iters

integer() Iteration number.

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingRepeatedSpCVEnv$instantiate(task)

Arguments

task

Task A task to instantiate.

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingRepeatedSpCVEnv$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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. 10.1101/357798.

Examples

# NOT RUN {
if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  library(mlr3)
  task = tsk("ecuador")

  # Instantiate Resampling
  rrcv = rsmp("repeated_spcv_env", folds = 4, repeats = 2)
  rrcv$instantiate(task)

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

  # Internal storage:
  rrcv$instance
}
# }