Convert to a Spatiotemporal Regression Task — as_task_regr

Convert object to a TaskRegrST.

This is a S3 generic, specialized for at least the following objects:

TaskRegrST: Ensure the identity.
data.frame() and DataBackend: Provides an alternative to the constructor of TaskRegrST.
sf::sf: Extracts spatial meta data before construction.
TaskClassif: Calls convert_task().

Usage

# S3 method for TaskClassifST
as_task_regr_st(
  x,
  target = NULL,
  drop_original_target = FALSE,
  drop_levels = TRUE,
  ...
)

as_task_regr_st(x, ...)

# S3 method for TaskRegrST
as_task_regr_st(x, clone = FALSE, ...)

# S3 method for data.frame
as_task_regr_st(
  x,
  target,
  id = deparse(substitute(x)),
  coordinate_names,
  crs = NA_character_,
  coords_as_features = FALSE,
  label = NA_character_,
  ...
)

# S3 method for DataBackend
as_task_regr_st(
  x,
  target,
  id = deparse(substitute(x)),
  positive = NULL,
  coordinate_names,
  crs,
  coords_as_features = FALSE,
  label = NA_character_,
  ...
)

# S3 method for sf
as_task_regr_st(
  x,
  target = NULL,
  id = deparse(substitute(x)),
  coords_as_features = FALSE,
  label = NA_character_,
  ...
)

# S3 method for TaskClassifST
as_task_regr_st(
  x,
  target = NULL,
  drop_original_target = FALSE,
  drop_levels = TRUE,
  ...
)

Arguments

x: (any)
Object to convert.
target: (character(1))
Name of the target column.
drop_original_target: (logical(1))
If FALSE (default), the original target is added as a feature. Otherwise the original target is dropped.
drop_levels: (logical(1))
If TRUE (default), unused levels of the new target variable are dropped.
...: (any)
Additional arguments.
clone: (logical(1))
If TRUE, ensures that the returned object is not the same as the input x.
id: (character(1))
Id for the new task. Defaults to the (deparsed and substituted) name of the data argument.
coordinate_names: (character(1))
The column names of the coordinates in the data.
crs: (character(1))
Coordinate reference system. WKT2 or EPSG string.
coords_as_features: (logical(1))
If TRUE, coordinates are used as features. This is a shortcut for task$set_col_roles(c("x", "y"), role = "feature") with the assumption that the coordinates in the data are named "x" and "y".
label: (character(1))
Label for the new instance. Shown in as.data.table(mlr_tasks).
positive: (character(1))
Only for binary classification: Name of the positive class. The levels of the target columns are reordered accordingly, so that the first element of $class_names is the positive class, and the second element is the negative class.

Value

TaskRegrST

Examples

# \donttest{
if (mlr3misc::require_namespaces(c("sf"), quietly = TRUE)) {
  library("mlr3")
  data("cookfarm_mlr3", package = "mlr3spatiotempcv")

  # data.frame
  as_task_regr_st(cookfarm_mlr3, target = "PHIHOX",
    coords_as_features = FALSE, crs = 26911,
    coordinate_names = c("x", "y"))

  # sf
  cookfarm_sf = sf::st_as_sf(cookfarm_mlr3, coords = c("x", "y"), crs = 26911)
  as_task_regr_st(cookfarm_sf, target = "PHIHOX")
}
#> <TaskRegrST:cookfarm_sf> (178840 x 23)
#> * Target: PHIHOX
#> * Properties: -
#> * Features (22):
#>   - dbl (18): BLD, LHDICM, Port1C, Port1EC, Port1VW, Port2C, Port2EC,
#>     Port2VW, Port3C, Port3EC, Port3VW, Port4C, Port4EC, Port4VW,
#>     Port5C, Port5EC, Port5VW, UHDICM
#>   - fct (3): HZDUSD, SOURCEID, TAXSUSDA
#>   - chr (1): Date
#> * Coordinates:
#>                X       Y
#>            <num>   <num>
#>      1: 493383.1 5180586
#>      2: 493383.1 5180586
#>      3: 493383.1 5180586
#>      4: 493383.1 5180586
#>      5: 493383.1 5180586
#>     ---                 
#> 178836: 493841.6 5181101
#> 178837: 493841.6 5181101
#> 178838: 493841.6 5181101
#> 178839: 493841.6 5181101
#> 178840: 493841.6 5181101
# }