mlr_pipeops_chunk | R Documentation |
Chunks its input into outnum
chunks.
Creates outnum
Task
s during training, and
simply passes on the input during outnum
times during prediction.
R6Class
object inheriting from PipeOp
.
PipeOpChunk$new(outnum, id = "chunk", param_vals = list())
outnum
:: numeric(1)
Number of output channels, and therefore number of chunks created.
id
:: character(1)
Identifier of resulting object, default "chunk"
.
param_vals
:: named list
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list()
.
PipeOpChunk
has one input channel named "input"
, taking a Task
both during training and prediction.
PipeOpChunk
has multiple output channels depending on the options
construction argument, named "output1"
, "output2"
, ...
All output channels produce (respectively disjoint, random) subsets of the input Task
during training, and
pass on the original Task
during prediction.
The $state
is left empty (list()
).
shuffle
:: logical(1)
Should the data be shuffled before chunking? Initialized to TRUE
.
Uses the mlr3misc::chunk_vector()
function.
Only fields inherited from PipeOp
.
Only methods inherited from PipeOp
.
https://mlr-org.com/pipeops.html
Other PipeOps:
PipeOp
,
PipeOpEnsemble
,
PipeOpImpute
,
PipeOpTargetTrafo
,
PipeOpTaskPreproc
,
PipeOpTaskPreprocSimple
,
mlr_pipeops
,
mlr_pipeops_adas
,
mlr_pipeops_blsmote
,
mlr_pipeops_boxcox
,
mlr_pipeops_branch
,
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_encode
,
mlr_pipeops_encodeimpact
,
mlr_pipeops_encodelmer
,
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_rowapply
,
mlr_pipeops_scale
,
mlr_pipeops_scalemaxabs
,
mlr_pipeops_scalerange
,
mlr_pipeops_select
,
mlr_pipeops_smote
,
mlr_pipeops_smotenc
,
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
library("mlr3")
task = tsk("wine")
opc = mlr_pipeops$get("chunk", 2)
# watch the row number: 89 during training (task is chunked)...
opc$train(list(task))
# ... 178 during predict (task is copied)
opc$predict(list(task))
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