mlr_resamplings_repeated_spcv_tiles | R Documentation |
Spatial partitioning using rectangular tiles.
Small partitions can optionally be merged into adjacent ones to avoid
partitions with too few observations.
This method is similar to ResamplingSpCVBlock
by making use of
rectangular zones in the coordinate space.
See the upstream implementation at sperrorest::partition_disc()
and
Brenning (2012) for further information.
dsplit
(integer(2)
)
Equidistance of splits in (possibly rotated) x direction (dsplit[1]
) and y direction (dsplit[2]
) used to define tiles.
If dsplit is of length 1, its value is recycled.
Either dsplit
or nsplit
must be specified.
nsplit
(integer(2)
)
Number of splits in (possibly rotated) x direction (nsplit[1]
) and y direction (nsplit[2]
) used to define tiles.
If nsplit
is of length 1, its value is recycled.
rotation
(character(1)
)
Whether and how the rectangular grid should be rotated; random rotation is only possible between -45 and +45 degrees.
Accepted values: One of c("none", "random", "user")
.
user_rotation
(character(1)
)
Only used when rotation = "user"
.
Angle(s) (in degrees) by which the rectangular grid is to be rotated in
each repetition.
Either a vector of same length as repeats
, or a single number that
will be replicated length(repeats)
times.
offset
(logical(1)
)
Whether and how the rectangular grid should be shifted by an offset.
Accepted values: One of c("none", "random", "user")
.
user_offset
(logical(1)
)
Only used when offset = "user"
.
A list (or vector) of two components specifying a shift of the rectangular
grid in (possibly rotated) x and y direction.
The offset values are relative values, a value of 0.5 resulting in a
one-half tile shift towards the left, or upward.
If this is a list, its first (second) component refers to the rotated
x (y) direction, and both components must have same length as repeats
(or length 1).
If a vector of length 2 (or list components have length 1), the two values
will be interpreted as relative shifts in (rotated) x and y direction,
respectively, and will therefore be recycled as needed (length(repeats)
times each).
reassign
(logical(1)
)
If TRUE
, 'small' tiles (as per min_frac
and min_n
) are merged with
(smallest) adjacent tiles.
If FALSE
, small tiles are 'eliminated', i.e., set to NA.
min_frac
(numeric(1)
)
Value must be >=0, <1.
Minimum relative size of partition as percentage of sample.
min_n
(integer(1)
)
Minimum number of samples per partition.
iterate
(integer(1)
)
Passed down to sperrorest::tile_neighbors()
.
repeats
(integer(1)
)
Number of repeats.
mlr3::Resampling
-> ResamplingRepeatedSpCVTiles
iters
integer(1)
Returns the number of resampling iterations, depending on the
values stored in the param_set
.
new()
Create a "Spatial 'Tiles' resampling" resampling instance.
For a list of available arguments, please see sperrorest::partition_tiles.
ResamplingRepeatedSpCVTiles$new(id = "repeated_spcv_tiles")
id
character(1)
Identifier for the resampling strategy.
folds()
Translates iteration numbers to fold number.
ResamplingRepeatedSpCVTiles$folds(iters)
iters
integer()
Iteration number.
repeats()
Translates iteration numbers to repetition number.
ResamplingRepeatedSpCVTiles$repeats(iters)
iters
integer()
Iteration number.
instantiate()
Materializes fixed training and test splits for a given task.
ResamplingRepeatedSpCVTiles$instantiate(task)
task
Task
A task to instantiate.
clone()
The objects of this class are cloneable with this method.
ResamplingRepeatedSpCVTiles$clone(deep = FALSE)
deep
Whether to make a deep clone.
Brenning A (2012). “Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest.” In 2012 IEEE International Geoscience and Remote Sensing Symposium. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/igarss.2012.6352393")}.
ResamplingSpCVBlock
if (mlr3misc::require_namespaces("sperrorest", quietly = TRUE)) {
library(mlr3)
task = tsk("ecuador")
# Instantiate Resampling
rrcv = rsmp("repeated_spcv_tiles",
repeats = 2,
nsplit = c(4L, 3L), reassign = FALSE)
rrcv$instantiate(task)
# Individual sets:
rrcv$iters
rrcv$folds(10:12)
rrcv$repeats(10:12)
# Individual sets:
rrcv$train_set(1)
rrcv$test_set(1)
intersect(rrcv$train_set(1), rrcv$test_set(1))
# Internal storage:
rrcv$instance # table
}
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