mlr3spatiotempcv (version 2.0.3)

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

Description

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

Parameters

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

• stratify
  If TRUE, stratify on the target column.

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

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

• ResamplingRepeatedSptCVCstf$new()

• ResamplingRepeatedSptCVCstf$folds()

• ResamplingRepeatedSptCVCstf$repeats()

• ResamplingRepeatedSptCVCstf$instantiate()

• ResamplingRepeatedSptCVCstf$clone()

Inherited methods

• mlr3::Resampling$format()
• mlr3::Resampling$help()
• mlr3::Resampling$print()
• mlr3::Resampling$test_set()
• mlr3::Resampling$train_set()

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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

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.

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.

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").

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