Copies its input
outnum times. This PipeOp usually not needed,
because copying happens automatically when one
PipeOp is followed
by multiple different
PipeOps. However, when constructing big
Graphs using the
PipeOpCopy can be helpful to
PipeOp gets connected to which.
R6Class object inheriting from
PipeOpCopy$new(outnum, id = "copy", param_vals = list())
Number of output channels, and therefore number of copies being made.
Identifier of resulting object, default
param_vals :: named
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default
PipeOpCopy has one input channel named
"input", taking any input (
"*") both during training and prediction.
PipeOpCopy has multiple output channels depending on the
outnum construction argument, named
All output channels produce the object given as input (
$state is left empty (
PipeOpCopy has no parameters.
Note that copies are not clones, but only reference copies. This affects R6-objects: If R6 objects are copied using PipeOpCopy, they must be cloned before
Only fields inherited from
Only methods inherited from
Other Placeholder Pipeops:
# The following copies the output of 'scale' automatically to both # 'pca' and 'nop' po("scale") %>>% gunion(list( po("pca"), po("nop") )) #> Graph with 3 PipeOps: #> ID State sccssors prdcssors #> scale <<UNTRAINED>> pca,nop #> pca <<UNTRAINED>> scale #> nop <<UNTRAINED>> scale # The following would not work: the '%>>%'-operator does not know # which output to connect to which input # > gunion(list( # > po("scale"), # > po("select") # > )) %>>% # > gunion(list( # > po("pca"), # > po("nop"), # > po("imputemean") # > )) # Instead, the 'copy' operator makes clear which output gets copied. gunion(list( po("scale") %>>% mlr_pipeops$get("copy", outnum = 2), po("select") )) %>>% gunion(list( po("pca"), po("nop"), po("imputemean") )) #> Graph with 6 PipeOps: #> ID State sccssors prdcssors #> scale <<UNTRAINED>> copy #> select <<UNTRAINED>> imputemean #> copy <<UNTRAINED>> pca,nop scale #> imputemean <<UNTRAINED>> select #> pca <<UNTRAINED>> copy #> nop <<UNTRAINED>> copy