Changes the target of a Task
according to a function given as hyperparameter.
An inverter-function that undoes the transformation during prediction must also be given.
R6Class
object inheriting from PipeOpTargetTrafo
/PipeOp
PipeOpTargetMutate$new(id = "targetmutate", param_vals = list(), new_task_type = NULL)
id
:: character(1)
Identifier of resulting object, default "targetmutate"
.
param_vals
:: named list
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise
be set during construction. Default list()
.
new_task_type
:: character(1)
| NULL
The task type to which the output is converted, must be one of mlr_reflections$task_types$type
.
Defaults to NULL
: no change in task type.
Input and output channels are inherited from PipeOpTargetTrafo
.
The $state
is left empty (list()
).
The parameters are the parameters inherited from PipeOpTargetTrafo
, as well as:
trafo
:: function
data.table
-> data.table
Transformation function for the target. Should only be a function of the target, i.e., taking a
single data.table
argument, typically with one column. The return value is used as the new
target of the resulting Task
. To change target names, change the column name of the data
using e.g. setnames()
.
Note that this function also gets called during prediction and should thus gracefully handle NA
values.
Initialized to identity()
.
inverter
:: function
data.table
-> data.table
| named list
Inversion of the transformation function for the target. Called on a data.table
created from a Prediction
using as.data.table()
, without the $row_ids
and $truth
columns,
and should return a data.table
or named list
that contains the new relevant slots of a
Prediction
subclass (e.g., $response
, $prob
, $se
, ...). Initialized to identity()
.
Overloads PipeOpTargetTrafo
's .transform()
and
.invert()
functions. Should be used in combination with PipeOpTargetInvert
.
Fields inherited from PipeOp
, as well as:
new_task_type
:: character(1)
new_task_type
construction argument. Read-only.
Only methods inherited from PipeOpTargetTrafo
/PipeOp
.
Other PipeOps:
PipeOpEnsemble
,
PipeOpImpute
,
PipeOpTargetTrafo
,
PipeOpTaskPreprocSimple
,
PipeOpTaskPreproc
,
PipeOp
,
mlr_pipeops_boxcox
,
mlr_pipeops_branch
,
mlr_pipeops_chunk
,
mlr_pipeops_classbalancing
,
mlr_pipeops_classifavg
,
mlr_pipeops_classweights
,
mlr_pipeops_colapply
,
mlr_pipeops_collapsefactors
,
mlr_pipeops_colroles
,
mlr_pipeops_copy
,
mlr_pipeops_datefeatures
,
mlr_pipeops_encodeimpact
,
mlr_pipeops_encodelmer
,
mlr_pipeops_encode
,
mlr_pipeops_featureunion
,
mlr_pipeops_filter
,
mlr_pipeops_fixfactors
,
mlr_pipeops_histbin
,
mlr_pipeops_ica
,
mlr_pipeops_imputeconstant
,
mlr_pipeops_imputehist
,
mlr_pipeops_imputelearner
,
mlr_pipeops_imputemean
,
mlr_pipeops_imputemedian
,
mlr_pipeops_imputemode
,
mlr_pipeops_imputeoor
,
mlr_pipeops_imputesample
,
mlr_pipeops_kernelpca
,
mlr_pipeops_learner
,
mlr_pipeops_missind
,
mlr_pipeops_modelmatrix
,
mlr_pipeops_multiplicityexply
,
mlr_pipeops_multiplicityimply
,
mlr_pipeops_mutate
,
mlr_pipeops_nmf
,
mlr_pipeops_nop
,
mlr_pipeops_ovrsplit
,
mlr_pipeops_ovrunite
,
mlr_pipeops_pca
,
mlr_pipeops_proxy
,
mlr_pipeops_quantilebin
,
mlr_pipeops_randomprojection
,
mlr_pipeops_randomresponse
,
mlr_pipeops_regravg
,
mlr_pipeops_removeconstants
,
mlr_pipeops_renamecolumns
,
mlr_pipeops_replicate
,
mlr_pipeops_scalemaxabs
,
mlr_pipeops_scalerange
,
mlr_pipeops_scale
,
mlr_pipeops_select
,
mlr_pipeops_smote
,
mlr_pipeops_spatialsign
,
mlr_pipeops_subsample
,
mlr_pipeops_targetinvert
,
mlr_pipeops_targettrafoscalerange
,
mlr_pipeops_textvectorizer
,
mlr_pipeops_threshold
,
mlr_pipeops_tunethreshold
,
mlr_pipeops_unbranch
,
mlr_pipeops_updatetarget
,
mlr_pipeops_vtreat
,
mlr_pipeops_yeojohnson
,
mlr_pipeops
# NOT RUN { library(mlr3) task = tsk("boston_housing") po = PipeOpTargetMutate$new("logtrafo", param_vals = list( trafo = function(x) log(x, base = 2), inverter = function(x) list(response = 2 ^ x$response)) ) # Note that this example is ill-equipped to work with # `predict_type == "se"` predictions. po$train(list(task)) po$predict(list(task)) g = Graph$new() g$add_pipeop(po) g$add_pipeop(LearnerRegrRpart$new()) g$add_pipeop(PipeOpTargetInvert$new()) g$add_edge(src_id = "logtrafo", dst_id = "targetinvert", src_channel = 1, dst_channel = 1) g$add_edge(src_id = "logtrafo", dst_id = "regr.rpart", src_channel = 2, dst_channel = 1) g$add_edge(src_id = "regr.rpart", dst_id = "targetinvert", src_channel = 1, dst_channel = 2) g$train(task) g$predict(task) #syntactic sugar using ppl(): tt = ppl("targettrafo", graph = 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) # }