mlr_resamplings_repeated_sptcv_cstf function

(CAST) Repeated spatiotemporal "leave-location-and-time-out" resampling

Splits data using Leave-Location-Out (LLO), Leave-Time-Out (LTO) and Leave-Location-and-Time-Out (LLTO) partitioning. See the upstream implementation at CreateSpacetimeFolds()

(package list("CAST")) and Meyer et al. (2018) for further information.

Details

LLO predicts on unknown locations i.e. complete locations are left out in the training sets. The "space" role in Task$col_roles identifies spatial units. If stratify is TRUE, the target distribution is similar in each fold. This is useful for land cover classification when the observations are polygons. In this case, LLO with stratification should be used to hold back complete polygons and have a similar target distribution in each fold. LTO leaves out complete temporal units which are identified by the "time" role in Task$col_roles. LLTO leaves out spatial and temporal units. See the examples.

Parameters

• folds (integer(1))

  Number of folds.

• stratify

  If TRUE, stratify on the target column.

• repeats (integer(1))

  Number of repeats.

Examples

library(mlr3)
task = tsk("cookfarm_mlr3")
task$set_col_roles("SOURCEID", roles = "space")
task$set_col_roles("Date", roles = "time")

# Instantiate Resampling
rcv = rsmp("repeated_sptcv_cstf", folds = 5, repeats = 2)
rcv$instantiate(task)

### Individual sets:
# rcv$train_set(1)
# rcv$test_set(1)
# check that no obs are in both sets
intersect(rcv$train_set(1), rcv$test_set(1)) # good!

# Internal storage:
# rcv$instance # table

References

Zhao Y, Karypis G (2002). Evaluation of Hierarchical Clustering Algorithms for Document Datasets.

11th Conference of Information and Knowledge Management (CIKM), 51-524. tools:::Rd_expr_doi("https://doi.org/10.1145/584792.584877") .

Super class

mlr3::Resampling -> ResamplingRepeatedSptCVCstf

Active bindings

• iters: integer(1)

   Returns the number of resampling iterations, depending on the values stored in the `param_set`.
  

Methods

Public methods

• list("ResamplingRepeatedSptCVCstf$new()")

• list("ResamplingRepeatedSptCVCstf$folds()")

• list("ResamplingRepeatedSptCVCstf$repeats()")

• list("ResamplingRepeatedSptCVCstf$instantiate()")

• list("ResamplingRepeatedSptCVCstf$clone()")

Method new()

Create a "Spacetime Folds" resampling instance.

Usage

ResamplingRepeatedSptCVCstf$new(id = "repeated_sptcv_cstf")

Arguments

• id: character(1)

   Identifier for the resampling strategy.
  

Method folds()

Translates iteration numbers to fold number.

Usage

ResamplingRepeatedSptCVCstf$folds(iters)

Arguments

• iters: integer()

   Iteration number.
  

Method repeats()

Translates iteration numbers to repetition number.

Usage

ResamplingRepeatedSptCVCstf$repeats(iters)

Arguments

• iters: integer()

   Iteration number.
  

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingRepeatedSptCVCstf$instantiate(task)

Arguments

• task: mlr3::Task

   A task to instantiate.
  

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingRepeatedSptCVCstf$clone(deep = FALSE)

Arguments

• deep: Whether to make a deep clone.