mlr3spatiotempcv (version 2.0.2)

mlr_resamplings_repeated_spcv_coords: (sperrorest) Repeated coordinate-based k-means clustering

Description

Splits data by clustering in the coordinate space. See the upstream implementation at sperrorest::partition_kmeans() and Brenning (2012) for further information.

Parameters

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

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

Super class

mlr3::Resampling -> ResamplingRepeatedSpCVCoords

Active bindings

iters

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

Methods

Public methods

• ResamplingRepeatedSpCVCoords$new()

• ResamplingRepeatedSpCVCoords$folds()

• ResamplingRepeatedSpCVCoords$repeats()

• ResamplingRepeatedSpCVCoords$instantiate()

• ResamplingRepeatedSpCVCoords$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 an "coordinate-based" repeated resampling instance.

For a list of available arguments, please see sperrorest::partition_cv.

Usage

ResamplingRepeatedSpCVCoords$new(id = "repeated_spcv_coords")

Arguments

id

character(1)
Identifier for the resampling strategy.

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Method folds()

Translates iteration numbers to fold number.

Usage

ResamplingRepeatedSpCVCoords$folds(iters)

Arguments

iters

integer()
Iteration number.

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Method repeats()

Translates iteration numbers to repetition number.

Usage

ResamplingRepeatedSpCVCoords$repeats(iters)

Arguments

iters

integer()
Iteration number.

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Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingRepeatedSpCVCoords$instantiate(task)

Arguments

task

Task
A task to instantiate.

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Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingRepeatedSpCVCoords$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Details

Universal partitioning method that splits the data in the coordinate space. Useful for spatially homogeneous datasets that cannot be split well with rectangular approaches like ResamplingSpCVBlock.

References

Brenning A (2012). “Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest.” In 2012 IEEE International Geoscience and Remote Sensing Symposium. tools:::Rd_expr_doi("10.1109/igarss.2012.6352393").

Examples

library(mlr3)
task = tsk("diplodia")

# Instantiate Resampling
rrcv = rsmp("repeated_spcv_coords", folds = 3, repeats = 5)
rrcv$instantiate(task)

# Individual sets:
rrcv$iters
rrcv$folds(1:6)
rrcv$repeats(1:6)

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

# Internal storage:
rrcv$instance # table