Visualization Functions for SptCV Cstf Methods.

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

Usage

# S3 method for ResamplingSptCVCstf
autoplot(
  object,
  task,
  fold_id = NULL,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  repeats_id = NULL,
  tickformat_date = "%Y-%m",
  nticks_x = 3,
  nticks_y = 3,
  point_size = 3,
  axis_label_fontsize = 11,
  static_image = FALSE,
  show_omitted = FALSE,
  plot3D = NULL,
  plot_time_var = NULL,
  sample_fold_n = NULL,
  ...
)

# S3 method for ResamplingRepeatedSptCVCstf
autoplot(
  object,
  task,
  fold_id = NULL,
  repeats_id = 1,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  tickformat_date = "%Y-%m",
  nticks_x = 3,
  nticks_y = 3,
  point_size = 3,
  axis_label_fontsize = 11,
  plot3D = NULL,
  plot_time_var = NULL,
  ...
)

# S3 method for ResamplingSptCVCstf
plot(x, ...)

# S3 method for ResamplingRepeatedSptCVCstf
plot(x, ...)

Arguments

object

[Resampling]
mlr3 spatial resampling object of class ResamplingSptCVCstf or ResamplingRepeatedSptCVCstf.

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 a list with of 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.

repeats_id

[numeric]
Repetition ID to plot.

tickformat_date

[character]
Date format for z-axis.

nticks_x

[integer]
Number of x axis breaks.

nticks_y

[integer]
Number of y axis breaks.

point_size

[numeric]
Point size of markers.

axis_label_fontsize

[integer]
Font size of axis labels.

static_image

[logical]
Whether to create a static image from the plotly plot via plotly::orca(). This requires the orca utility to be available. See https://github.com/plotly/orca for more information. When used, by default a file named plot.png is created in the current working directory.

show_omitted

[logical]
Whether to show points not used in train or test set for the current fold.

plot3D

[logical]
Whether to create a 2D image via ggplot2 or a 3D plot via plotly.

plot_time_var

[character]
The variable to use for the z-axis (time). Remove the column role feature for this variable to only use it for plotting.

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.

...

Passed down to plotly::orca(). Only effective when static_image = TRUE.

x

[Resampling]
mlr3 spatial resampling object of class ResamplingSptCVCstf or ResamplingRepeatedSptCVCstf.

Details

This method requires to set argument fold_id. No plot showing all folds in one plot can be created. This is because the LLTO method does not make use of all observations but only a subset of them (many observations are omitted). Hence, train and test sets of one fold are not re-used in other folds as in other methods and plotting these without a train/test indicator would be misleading.

2D vs 3D plotting

This method has both a 2D and a 3D plotting method. The 2D method returns a ggplot with x and y axes representing the spatial coordinates. The 3D method uses plotly to create an interactive 3D plot. Set plot3D = TRUE to use the 3D method.

Note that spatiotemporal datasets usually suffer from overplotting in 2D mode.

See also

• mlr3book chapter on "Spatiotemporal Visualization"

• Vignette Spatiotemporal Visualization.

• autoplot.ResamplingSpCVBlock()

• autoplot.ResamplingSpCVBuffer()

• autoplot.ResamplingSpCVCoords()

• autoplot.ResamplingSpCVEnv()

• autoplot.ResamplingCV()

Examples

# \donttest{
if (mlr3misc::require_namespaces(c("sf", "plotly"), quietly = TRUE)) {
  library(mlr3)
  library(mlr3spatiotempcv)
  task_st = tsk("cookfarm_mlr3")
  task_st$set_col_roles("SOURCEID", "space")
  task_st$set_col_roles("Date", "time")
  resampling = rsmp("sptcv_cstf", folds = 5)
  resampling$instantiate(task_st)

  # with both `"space"` and `"time"` column roles set (LLTO), the omitted
  # observations per fold can be shown by setting `show_omitted = TRUE`
  autoplot(resampling, task_st, fold_id = 1, show_omitted = TRUE)
}

# }