mlr3spatiotempcv (version 2.3.2)

mlr_resamplings_sptcv_cstf: (CAST) 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.

Super class

mlr3::Resampling -> ResamplingSptCVCstf

Active bindings

iters

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

Methods

Public methods

• ResamplingSptCVCstf$new()

• ResamplingSptCVCstf$instantiate()

• ResamplingSptCVCstf$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

ResamplingSptCVCstf$new(id = "sptcv_cstf")

Arguments

id

character(1)
Identifier for the resampling strategy.

---

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSptCVCstf$instantiate(task)

Arguments

task

mlr3::Task
A task to instantiate.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSptCVCstf$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

Meyer H, Reudenbach C, Hengl T, Katurji M, Nauss T (2018). “Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation.” Environmental Modelling & Software, 101, 1--9. tools:::Rd_expr_doi("10.1016/j.envsoft.2017.12.001").

Examples

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

# Instantiate Resampling
rcv = rsmp("sptcv_cstf", folds = 5)
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
# }