Shorthand PipeOp Constructor

Create

• a PipeOp from mlr_pipeops from given ID

• a PipeOpLearner from a Learner object

• a PipeOpFilter from a Filter object

• a PipeOpSelect from a Selector object

• a clone of a PipeOp from a given PipeOp (possibly with changed settings)

The object is initialized with given parameters and param_vals.

po() taks a single obj (PipeOp id, Learner, ...) and converts it to a PipeOp. pos() (with plural-s) takes either a character-vector, or a list of objects, and creates a list of PipeOps.

Usage

po(.obj, ...)

pos(.objs, ...)

Arguments

.obj

[any]
The object from which to construct a PipeOp. If this is a character(1), it is looked up in the mlr_pipeops dictionary. Otherwise, it is converted to a PipeOp.

...

any
Additional parameters to give to constructed object. This may be an argument of the constructor of the PipeOp, in which case it is given to this constructor; or it may be a parameter value, in which case it is given to the param_vals argument of the constructor.

.objs

character | list
Either a character of PipeOps to look up in mlr_pipeops, or a list of other objects to be converted to a PipeOp. If this is a named list, then the names are used as $id slot for the resulting PipeOps.

Value

A PipeOp (for po()), or a list of PipeOps (for pos()).

Examples

library("mlr3")

po("learner", lrn("classif.rpart"), cp = 0.3)
#> PipeOp: <classif.rpart> (not trained)
#> values: <cp=0.3, xval=0>
#> Input channels <name [train type, predict type]>:
#>   input [TaskClassif,TaskClassif]
#> Output channels <name [train type, predict type]>:
#>   output [NULL,PredictionClassif]

po(lrn("classif.rpart"), cp = 0.3)
#> PipeOp: <classif.rpart> (not trained)
#> values: <cp=0.3, xval=0>
#> Input channels <name [train type, predict type]>:
#>   input [TaskClassif,TaskClassif]
#> Output channels <name [train type, predict type]>:
#>   output [NULL,PredictionClassif]

# is equivalent with:
mlr_pipeops$get("learner", lrn("classif.rpart"),
  param_vals = list(cp = 0.3))
#> PipeOp: <classif.rpart> (not trained)
#> values: <cp=0.3, xval=0>
#> Input channels <name [train type, predict type]>:
#>   input [TaskClassif,TaskClassif]
#> Output channels <name [train type, predict type]>:
#>   output [NULL,PredictionClassif]

mlr3pipelines::pos(c("pca", original = "nop"))
#> $pca
#> PipeOp: <pca> (not trained)
#> values: <list()>
#> Input channels <name [train type, predict type]>:
#>   input [Task,Task]
#> Output channels <name [train type, predict type]>:
#>   output [Task,Task]
#> 
#> $original
#> PipeOp: <original> (not trained)
#> values: <list()>
#> Input channels <name [train type, predict type]>:
#>   input [*,*]
#> Output channels <name [train type, predict type]>:
#>   output [*,*]
#>