mlr_pipeops {mlr3pipelines} | R Documentation |
Dictionary of PipeOps
Description
A simple Dictionary
storing objects of class PipeOp
.
Each PipeOp
has an associated help page, see mlr_pipeops_[id]
.
Format
R6Class
object inheriting from mlr3misc::Dictionary
.
Fields
Fields inherited from Dictionary
, as well as:
-
metainf
::environment
Environment that stores themetainf
argument of the$add()
method. Only for internal use.
Methods
Methods inherited from Dictionary
, as well as:
-
add(key, value, metainf = NULL)
(character(1)
,R6ClassGenerator
,NULL
|list
)
Adds constructorvalue
to the dictionary with keykey
, potentially overwriting a previously stored item. Ifmetainf
is notNULL
(the default), it must be alist
of arguments that will be given to thevalue
constructor (i.e.value$new()
) when it needs to be constructed foras.data.table
PipeOp
listing.
S3 methods
-
as.data.table(dict)
Dictionary
->data.table::data.table
Returns adata.table
with the following columns:-
key
:: (character
)
Key with which thePipeOp
was registered to theDictionary
using the$add()
method. -
label
:: (character
)
Description of thePipeOp
's functionality. -
packages
:: (character
)
Set of all required packages for thePipeOp
's train and predict methods. -
tags
:: (character
)
A set of tags associated with thePipeOp
describing its purpose. -
feature_types
:: (character
)
Feature types thePipeOp
operates on. IsNA
forPipeOp
s that do not directly operate on a Task. -
input.num
,output.num
:: (integer
)
Number of thePipeOp
's input and output channels. IsNA
forPipeOp
s which accept a varying number of input and/or output channels depending a construction argument. Seeinput
andoutput
fields ofPipeOp
. -
input.type.train
,input.type.predict
,output.type.train
,output.type.predict
:: (character
)
Types that are allowed as input to or returned as output of thePipeOp
's$train()
and$predict()
methods.
A value ofNULL
means that a null object, e.g. no data, is taken as input or being returned as output. A value of "*
" means that any type is possible.
If bothinput.type.train
andoutput.type.train
or bothinput.type.predict
andoutput.type.predict
contain values enclosed by square brackets ("[
", "]
"), then the respective input or channel isMultiplicity
-aware. For more information, seeMultiplicity
.
-
See Also
Other mlr3pipelines backend related:
Graph
,
PipeOp
,
PipeOpTargetTrafo
,
PipeOpTaskPreproc
,
PipeOpTaskPreprocSimple
,
mlr_graphs
,
mlr_pipeops_updatetarget
Other PipeOps:
PipeOp
,
PipeOpEncodePL
,
PipeOpEnsemble
,
PipeOpImpute
,
PipeOpTargetTrafo
,
PipeOpTaskPreproc
,
PipeOpTaskPreprocSimple
,
mlr_pipeops_adas
,
mlr_pipeops_blsmote
,
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_decode
,
mlr_pipeops_encode
,
mlr_pipeops_encodeimpact
,
mlr_pipeops_encodelmer
,
mlr_pipeops_encodeplquantiles
,
mlr_pipeops_encodepltree
,
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_learner_pi_cvplus
,
mlr_pipeops_learner_quantiles
,
mlr_pipeops_missind
,
mlr_pipeops_modelmatrix
,
mlr_pipeops_multiplicityexply
,
mlr_pipeops_multiplicityimply
,
mlr_pipeops_mutate
,
mlr_pipeops_nearmiss
,
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_tomek
,
mlr_pipeops_tunethreshold
,
mlr_pipeops_unbranch
,
mlr_pipeops_updatetarget
,
mlr_pipeops_vtreat
,
mlr_pipeops_yeojohnson
Other Dictionaries:
mlr_graphs
Examples
library("mlr3")
mlr_pipeops$get("learner", lrn("classif.rpart"))
# equivalent:
po("learner", learner = lrn("classif.rpart"))
# all PipeOps currently in the dictionary:
as.data.table(mlr_pipeops)[, c("key", "input.num", "output.num", "packages")]