autoplot.ResamplingSptCVCstf: Visualization Functions for SptCV Cstf Methods.

Description

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.

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.

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.

Examples

Run this code

# \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)
}
# }