convertOMLDataSetToMlr {OpenML}

Convert an OpenML data set to mlr task.

Description

Converts an OMLDataSet to a Task.

Usage

convertOMLDataSetToMlr(
  obj,
  mlr.task.id = "<oml.data.name>",
  task.type = NULL,
  target = obj$desc$default.target.attribute,
  ignore.flagged.attributes = TRUE,
  drop.levels = TRUE,
  fix.colnames = TRUE,
  verbosity = NULL
)

Arguments

obj

[OMLDataSet]
The object that should be converted.

mlr.task.id

[character(1)]
Id string for Task object. The strings <oml.data.name>, <oml.data.id> and <oml.data.version> will be replaced by their respective values contained in the OMLDataSet object. Default is <oml.data.name>.

task.type

[character(1)]
As we only pass the data set, we need to define the task type manually. Possible are: “Supervised Classification”, “Supervised Regression”, “Survival Analysis”. Default is NULL which means to guess it from the target column in the data set. If that is a factor or a logical, we choose classification. If it is numeric we choose regression. In all other cases an error is thrown.

target

[character]
The target for the classification/regression task. Default is the default.target.attribute of the OMLDataSetDescription.

ignore.flagged.attributes

[logical(1)]
Should those features that are listed in the data set description slot “ignore.attribute” be removed? Default is TRUE.

drop.levels

[logical(1)]
Should empty factor levels be dropped in the data? Default is TRUE.

fix.colnames

[logical(1)]
Should colnames of the data be fixed using make.names? Default is TRUE.

verbosity

[integer(1)]
Print verbose output on console? Possible values are:
0: normal output,
1: info output,
2: debug output.
Default is set via setOMLConfig.

Value

[Task].

See Also

Other data set-related functions: OMLDataSetDescription, OMLDataSet, convertMlrTaskToOMLDataSet(), deleteOMLObject(), getOMLDataSet(), listOMLDataSets(), tagOMLObject(), uploadOMLDataSet()

Examples

# \dontrun{
# 	library("mlr")
# 	autosOML = getOMLDataSet(data.id = 9)
# 	autosMlr = convertOMLDataSetToMlr(autosOML)
# }