R/doublecaret.R
grapes-greater-than-greater-than-grapes.RdThis operator “pipes” data from the source into the sink object.
If both objects are a CPO object, or both are a CPOTrained object,
they will be composed. A new object, representing the operation of performing both object's operations in succession,
will be created, which can be handled like a new CPO or CPOTrained object. See composeCPO.
If the source object is a data.frame or a link[mlr]{Task}, the
transformation operation will be applied to this data, and the same resulting
data will be returned. See applyCPO.
If the sink object is a Learner, the CPO will be attached to
this learner. The same operation will be performed during the train and
predict phase; the behaviour during the predict phase may furthermore
be depend on the training data. See attachCPO.
Note that you can not link a data.frame or Task directly
to a Learner, since this operation is not algebraically associative
with the composition of CPOs. Use train for this.
The %<<% operator is synonymous with %>>% with source and sink argument swapped.
The %>|% and %|<% operators perform piping followed by application of retrafo.
The %>|% evaluates the expression to its right before the expression to its left, so it may be
used in the most natural way without parentheses:
data %>|% cpo1 %>>% cpo2
is the same as
retrafo(data %>>% cpo1 %>>% cpo2).
The %<>>% and %<<<% operators perform the piping operation and assign the result
to the left hand variable. This way it is possible to apply a CPO, or to
attach a CPO to a Learner, and just keep the resulting
object. The assignment operators evaluate their right hand side before their left hand side, so
it is possible to build long chains that end up writing to the leftmost variable. Therefore the expression
data %<>>% cpo1 %<>>% cpo2 %>>% cpo3
is the same as
cpo1 = cpo1 %>>% cpo2 %>>% cpo3 data = data %>>% cpo1
cpo1 %>>% cpo2 cpo2 %<<% cpo1 cpo1 %<>>% cpo2 cpo2 %<<<% cpo1 cpo1 %>|% cpo2 cpo2 %|<% cpo1
| cpo1 | [ |
|---|---|
| cpo2 | [ |
[data.frame | Task | CPO | CPOTrained].
Other operators:
CPO,
applyCPO(),
as.list.CPO,
attachCPO(),
composeCPO(),
pipeCPO()
Other retrafo related:
CPOTrained,
NULLCPO,
applyCPO(),
as.list.CPO,
clearRI(),
getCPOClass(),
getCPOName(),
getCPOOperatingType(),
getCPOPredictType(),
getCPOProperties(),
getCPOTrainedCPO(),
getCPOTrainedCapability(),
getCPOTrainedState(),
is.retrafo(),
makeCPOTrainedFromState(),
pipeCPO(),
print.CPOConstructor()
Other inverter related:
CPOTrained,
NULLCPO,
applyCPO(),
as.list.CPO,
clearRI(),
getCPOClass(),
getCPOName(),
getCPOOperatingType(),
getCPOPredictType(),
getCPOProperties(),
getCPOTrainedCPO(),
getCPOTrainedCapability(),
getCPOTrainedState(),
is.inverter(),
makeCPOTrainedFromState(),
pipeCPO(),
print.CPOConstructor()
Other CPO lifecycle related:
CPOConstructor,
CPOLearner,
CPOTrained,
CPO,
NULLCPO,
attachCPO(),
composeCPO(),
getCPOClass(),
getCPOConstructor(),
getCPOTrainedCPO(),
identicalCPO(),
makeCPO()
# PCA-rotate pid.task rotated.pid.task = pid.task %>>% cpoScale() %>>% cpoPca() # Centering / Scaling *after* PCA newPCA = cpoPca() %>>% cpoScale() # Attach the above to learner pcaLogreg = newPCA %>>% makeLearner("classif.logreg") # append cpoAsNumeric to newPCA newPCA %<>>% cpoAsNumeric() print(newPCA)#> (pca >> scale >> as.numeric)(pca.center = TRUE, pca.scale = FALSE, scale.center = TRUE, scale.scale = TRUE)