mlr3spatiotempcv (version 0.1.1)

autoplot.ResamplingSpCVBlock: Visualization Functions for SpCV Block Methods.

Description

Generic S3 plot() and autoplot() (ggplot2) methods.

Usage

# S3 method for ResamplingSpCVBlock
autoplot(
  object,
  task,
  fold_id = NULL,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  crs = NULL,
  ...
)
# S3 method for ResamplingRepeatedSpCVBlock
autoplot(
  object,
  task,
  fold_id = NULL,
  repeats_id = NULL,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  crs = NULL,
  ...
)
# S3 method for ResamplingSpCVBlock
plot(x, ...)
# S3 method for ResamplingRepeatedSpCVBlock
plot(x, ...)

Arguments

object

[Resampling] mlr3 spatial resampling object of class ResamplingSpCVBlock or ResamplingRepeatedSpCVBlock.

task

[TaskClassifST]/[TaskRegrST] mlr3 task object.

fold_id

[numeric] Fold IDs to plot.

plot_as_grid

[logical(1)] Should a gridded plot using via patchwork be created? If FALSE only a list with all ggplot2 resamplings is returned. Only applies if a numeric vector is passed to argument fold_id.

train_color

[character(1)] The color to use for the training set observations.

test_color

[character(1)] The color to use for the test set observations.

crs

[character] EPSG code of the CRS for x and y axes.

...

Not used.

repeats_id

[numeric] Repetition ID to plot.

x

[Resampling] mlr3 spatial resampling object. One of class ResamplingSpCVBuffer, ResamplingSpCVBlock, ResamplingSpCVCoords, ResamplingSpCVEnv.

Value

ggplot() or list of ggplot2 objects.

Details

By default a plot is returned; if fold_id is set, a gridded plot is created. If plot_as_grid = FALSE, a list of plot objects is returned. This can be used to align the plots individually.

When no single fold is selected, the ggsci::scale_color_ucscgb() palette is used to display all partitions. If you want to change the colors, call <plot> + <color-palette>().

Examples

# NOT RUN {
if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  library(mlr3)
  library(mlr3spatiotempcv)
  task = tsk("ecuador")
  resampling = rsmp("spcv_block", range = 1000)
  resampling$instantiate(task)

  ## Visualize all partitions
  autoplot(resampling, task) +
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))

  ## Visualize the train/test split of a single fold
  autoplot(resampling, task, fold_id = 1)

  ## Visualize train/test splits of multiple folds
  autoplot(resampling, task, fold_id = c(1, 2))

  # list of ggplot2 resamplings
  plot_list = autoplot(resampling, task,
    fold_id = c(1, 2), grid = FALSE)
}
# }