mlr_pipeops_colapply | R Documentation |
Applies a function to each column of a task. Use the affect_columns
parameter inherited from
PipeOpTaskPreprocSimple
to limit the columns this function should be applied to. This can be used
for simple parameter transformations or type conversions (e.g. as.numeric
).
The same function is applied during training and prediction. One important relationship for
machine learning preprocessing is that during the prediction phase, the preprocessing on each
data row should be independent of other rows. Therefore, the applicator
function should always
return a vector / list where each result component only depends on the corresponding input component and
not on other components. As a rule of thumb, if the function f
generates output different
from Vectorize(f)
, it is not a function that should be used for applicator
.
R6Class
object inheriting from PipeOpTaskPreprocSimple
/PipeOpTaskPreproc
/PipeOp
.
PipeOpColApply$new(id = "colapply", param_vals = list())
id
:: character(1)
Identifier of resulting object, default "colapply"
.
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 PipeOpTaskPreprocSimple
.
The output is the input Task
with features changed according to the applicator
parameter.
The $state
is a named list
with the $state
elements inherited from PipeOpTaskPreprocSimple
.
The parameters are the parameters inherited from PipeOpTaskPreprocSimple
, as well as:
applicator
:: function
Function to apply to each column of the task.
The return value should be a vector
of the same length as the input, i.e., the function vectorizes over the input.
A typical example would be as.numeric
.
The return value can also be a matrix
, data.frame
, or data.table
.
In this case, the length of the input must match the number of returned rows.
The names of the resulting features of the output Task
is based on the (column) name(s) of the return value of the applicator function,
prefixed with the original feature name separated by a dot (.
).
Use Vectorize
to create a vectorizing function from any function that ordinarily only takes one element input.
Calls map
on the data, using the value of applicator
as f.
and coerces the output via as.data.table
.
Only fields inherited from PipeOpTaskPreprocSimple
/PipeOpTaskPreproc
/PipeOp
.
Only methods inherited from PipeOpTaskPreprocSimple
/PipeOpTaskPreproc
/PipeOp
.
https://mlr3book.mlr-org.com/list-pipeops.html
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_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_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
library("mlr3") task = tsk("iris") poca = po("colapply", applicator = as.character) poca$train(list(task))[[1]] # types are converted # function that does not vectorize f1 = function(x) { # we could use `ifelse` here, but that is not the point if (x > 1) { "a" } else { "b" } } poca$param_set$values$applicator = Vectorize(f1) poca$train(list(task))[[1]]$data() # only affect Petal.* columns poca$param_set$values$affect_columns = selector_grep("^Petal") poca$train(list(task))[[1]]$data() # function returning multiple columns f2 = function(x) { cbind(floor = floor(x), ceiling = ceiling(x)) } poca$param_set$values$applicator = f2 poca$param_set$values$affect_columns = selector_all() poca$train(list(task))[[1]]$data()
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