Create

  • a PipeOp from mlr_pipeops from given ID

  • a PipeOpLearner from a Learner object

  • a PipeOpFilter from a Filter object

The object is initialized with given parameters and param_vals.

po(.obj, ...)

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.

Examples

library("mlr3") po("learner", lrn("classif.rpart"), cp = 0.3)
#> PipeOp: <classif.rpart> (not trained) #> values: <xval=0, cp=0.3> #> 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: <xval=0, cp=0.3> #> 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: <xval=0, cp=0.3> #> Input channels <name [train type, predict type]>: #> input [TaskClassif,TaskClassif] #> Output channels <name [train type, predict type]>: #> output [NULL,PredictionClassif]