mlr_graphs_targettrafo  R Documentation 
Wraps a Graph
that transforms a target during training and inverts the transformation
during prediction. This is done as follows:
Specify a transformation and inversion function using any subclass of PipeOpTargetTrafo
, defaults to
PipeOpTargetMutate
, afterwards apply graph
.
At the very end, during prediction the transformation is inverted using PipeOpTargetInvert
.
To set a transformation and inversion function for PipeOpTargetMutate
see the
parameters trafo
and inverter
of the param_set
of the resulting Graph
.
Note that the input graph
is not explicitly checked to actually return a
Prediction
during prediction.
All input arguments are cloned and have no references in common with the returned Graph
.
pipeline_targettrafo(
graph,
trafo_pipeop = PipeOpTargetMutate$new(),
id_prefix = ""
)
graph 

trafo_pipeop 

id_prefix 

Graph
library("mlr3")
tt = pipeline_targettrafo(PipeOpLearner$new(LearnerRegrRpart$new()))
tt$param_set$values$targetmutate.trafo = function(x) log(x, base = 2)
tt$param_set$values$targetmutate.inverter = function(x) list(response = 2 ^ x$response)
# gives the same as
g = Graph$new()
g$add_pipeop(PipeOpTargetMutate$new(param_vals = list(
trafo = function(x) log(x, base = 2),
inverter = function(x) list(response = 2 ^ x$response))
)
)
g$add_pipeop(LearnerRegrRpart$new())
g$add_pipeop(PipeOpTargetInvert$new())
g$add_edge(src_id = "targetmutate", dst_id = "targetinvert",
src_channel = 1, dst_channel = 1)
g$add_edge(src_id = "targetmutate", dst_id = "regr.rpart",
src_channel = 2, dst_channel = 1)
g$add_edge(src_id = "regr.rpart", dst_id = "targetinvert",
src_channel = 1, dst_channel = 2)
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