When applying CPOs to data, the operation entails
saving the CPOTrained information that gets generated
to an attribute of the resulting object. This is a useful solution to
the problem that applying multiple CPOs should also lead to a retrafo
object that performs the same multiple operations. However, sometimes
this may lead to surprising and unwanted results when a CPO is applied
and not meant to be part of a trafo-retrafo machine learning pipeline,
e.g. for dropping columns that occur in training but not in prediction
data. In that case, it is necessary to reset the retrafo and
possibly inverter attributes of the data being used. This can
be done either by using retrafo(data) <- NULL, or by using
clearRI. clearRI clears both retrafo and
inverter attributes.
clearRI(data)
| data | [ |
|---|
[data.frame | Task | WrappedModel] the
data after stripping all retrafo and inverter attributes.
Other retrafo related:
CPOTrained,
NULLCPO,
%>>%(),
applyCPO(),
as.list.CPO,
getCPOClass(),
getCPOName(),
getCPOOperatingType(),
getCPOPredictType(),
getCPOProperties(),
getCPOTrainedCPO(),
getCPOTrainedCapability(),
getCPOTrainedState(),
is.retrafo(),
makeCPOTrainedFromState(),
pipeCPO(),
print.CPOConstructor()
Other inverter related:
CPOTrained,
NULLCPO,
%>>%(),
applyCPO(),
as.list.CPO,
getCPOClass(),
getCPOName(),
getCPOOperatingType(),
getCPOPredictType(),
getCPOProperties(),
getCPOTrainedCPO(),
getCPOTrainedCapability(),
getCPOTrainedState(),
is.inverter(),
makeCPOTrainedFromState(),
pipeCPO(),
print.CPOConstructor()
# without clearRI transformed = iris.task %>>% cpoPca() transformed2 = transformed %>>% cpoScale() retrafo(transformed2) # [RETRAFO pca]=>[RETRAFO scale]#> CPO Retrafo chain #> [RETRAFO pca(center = TRUE, scale = FALSE)] => #> [RETRAFO scale(center = TRUE, scale = TRUE)]transformed = iris.task %>>% cpoPca() transformed2 = clearRI(transformed) %>>% cpoScale() retrafo(transformed2) # [RETRAFO scale]#> CPO Retrafo chain #> [RETRAFO scale(center = TRUE, scale = TRUE)]