Impute features by fitting a Learner
for each feature.
Uses the features indicated by the context_columns
parameter as features to train the imputation Learner
.
Note this parameter is part of the PipeOpImpute
base class and explained there.
Additionally, only features supported by the learner can be imputed; i.e. learners of type
regr
can only impute features of type integer
and numeric
, while classif
can impute
features of type factor
, ordered
and logical
.
R6Class
object inheriting from PipeOpImpute
/PipeOp
.
PipeOpImputeLearner$new(learner, id = NULL, param_vals = list())
id
:: character(1)
Identifier of resulting object, default "impute."
, followed by the id
of the Learner
.
learner
:: Learner
| character(1)
Learner
to wrap, or a string identifying a Learner
in the mlr3::mlr_learners
Dictionary
.
The Learner
needs to be able to handle missing values, i.e. have the missings
property.
param_vals
:: named list
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list()
.
Input and output channels are inherited from PipeOpImpute
.
The output is the input Task
with missing values from all affected features imputed by the trained model.
The $state
is a named list
with the $state
elements inherited from PipeOpImpute
.
The $state$models
is a named list
of models
created by the Learner
's $.train()
function
for each column. If a column consists of missing values only during training, the model
is 0
or the levels of the
feature; these are used for sampling during prediction.
The parameters are the parameters inherited from PipeOpImpute
, in addition to the parameters of the Learner
used for imputation.
Uses the $train
and $predict
functions of the provided learner. Features that are entirely NA
are imputed as 0
or randomly sampled from available (factor
/ logical
) levels.
The Learner
does not necessarily need to handle missing values in cases
where context_columns
is chosen well (or there is only one column with missing values present).
Fields inherited from PipeOpTaskPreproc
/PipeOp
, as well as:
learner
:: Learner
Learner
that is being wrapped. Read-only.
learner_models
:: list
of Learner
| NULL
Learner
that is being wrapped. This list is named by features for which a Learner
was fitted, and
contains the same Learner
, but with different respective models for each feature. If this PipeOp
is not trained,
this is an empty list
. For features that were entirely NA
during training, the list
contains NULL
elements.
Only methods inherited from PipeOpImpute
/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_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_targetmutate
,
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
Other Imputation PipeOps:
PipeOpImpute
,
mlr_pipeops_imputeconstant
,
mlr_pipeops_imputehist
,
mlr_pipeops_imputemean
,
mlr_pipeops_imputemedian
,
mlr_pipeops_imputemode
,
mlr_pipeops_imputeoor
,
mlr_pipeops_imputesample
# NOT RUN {
library("mlr3")
task = tsk("pima")
task$missings()
po = po("imputelearner", lrn("regr.rpart"))
new_task = po$train(list(task = task))[[1]]
new_task$missings()
po$state$model
# }
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