autoplot.ResamplingSpCVBlock function

Visualization Functions for SpCV Block Methods.

Generic S3 plot() and autoplot() (ggplot2) methods to visualize mlr3 spatiotemporal resampling objects.

## S3 method for class 'ResamplingSpCVBlock'
autoplot(
  object,
  task,
  fold_id = NULL,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  show_blocks = FALSE,
  show_labels = FALSE,
  sample_fold_n = NULL,
  label_size = 2,
  ...
)

## S3 method for class 'ResamplingRepeatedSpCVBlock'
autoplot(
  object,
  task,
  fold_id = NULL,
  repeats_id = 1,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  show_blocks = FALSE,
  show_labels = FALSE,
  sample_fold_n = NULL,
  label_size = 2,
  ...
)

## S3 method for class 'ResamplingSpCVBlock'
plot(x, ...)

## S3 method for class '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 list("patchwork") be created? If FALSE

  a list with of list("ggplot2") objects 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.

• show_blocks: [logical(1)]

  Whether to show an overlay of the spatial blocks polygons.

• show_labels: [logical(1)]

  Whether to show an overlay of the spatial block IDs.

• sample_fold_n: [integer]

  Number of points in a random sample stratified over partitions. This argument aims to keep file sizes of resulting plots reasonable and reduce overplotting in dense datasets.

• label_size: [numeric(1)]

  Label size of block labels. Only applies for show_labels = TRUE.

• ...: Passed to geom_sf(). Helpful for adjusting point sizes and shapes.

• repeats_id: [numeric]

  Repetition ID to plot.

• x: [Resampling]

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

Returns

ggplot2::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

if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  library(mlr3)
  library(mlr3spatiotempcv)
  task = tsk("ecuador")
  resampling = rsmp("spcv_block", range = 1000L)
  resampling$instantiate(task)

  ## list of ggplot2 resamplings
  plot_list = autoplot(resampling, task,
    crs = 4326,
    fold_id = c(1, 2), plot_as_grid = FALSE)

  ## 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) +
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))

  ## Visualize train/test splits of multiple folds
  autoplot(resampling, task,
    fold_id = c(1, 2),
    show_blocks = TRUE) *
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
}

See Also

• mlr3book chapter on "Spatial Analysis"
• autoplot.ResamplingSpCVBuffer()
• autoplot.ResamplingSpCVCoords()
• autoplot.ResamplingSpCVEnv()
• autoplot.ResamplingSpCVDisc()
• autoplot.ResamplingSpCVTiles()
• autoplot.ResamplingCV()
• autoplot.ResamplingSptCVCstf()