PipeOpInfo prints its input to the console or a logger in a customizable way.
Users can define how specific object classes should be displayed using custom printer functions.
Construction
PipeOpInfo$new(id = "info", collect_multiplicity = FALSE, log_target = "lgr::mlr3/mlr3pipelines::info")id::character(1)
Identifier of resulting object, default "info"printer::list
Optional mapping from object classes to printer functions. Custom functions override default printer-functions.collect_multiplicity::logical(1)
IfTRUE, the input is aMultiplicitycollecting channel.Multiplicityinput/output is accepted and the members are aggregated.log_target::character(1)
Specifies how the input object is printed to the console. By default it is directed to a logger, whose address can be customized using the form<output>::<argument1>::<argument2>. Otherwise it can be printed as "message", "warning" or "cat". When set to "none", no customized information about the object will be printed.
Input and Output Channels
PipeOpInfo has one input channel called "input", it can take any type of input (*).
PipeOpInfo has one output channel called "output", it can take any type of output (*).
State
The $state is left empty (list()).
Internals
PipeOpInfo forwards its input unchanged, but prints information about it
depending on the printer and log_target settings.
Fields
Fields inherited from PipeOp, as well as:
printer::list
Mapping of object classes to printer functions. Includes printer-specifications forTask,Prediction,NULL. Otherwise object is printed as is.log_target::character(1)
Specifies current output target.
Methods
Only methods inherited from PipeOp.
See also
https://mlr-org.com/pipeops.html
Other PipeOps:
PipeOp,
PipeOpEncodePL,
PipeOpEnsemble,
PipeOpImpute,
PipeOpTargetTrafo,
PipeOpTaskPreproc,
PipeOpTaskPreprocSimple,
mlr_pipeops,
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_isomap,
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
Examples
library("mlr3")
poinfo = po("info")
poinfo$train(list(tsk("mtcars")))
#> $output
#>
#> ── <TaskRegr> (32x11): Motor Trends ────────────────────────────────────────────
#> • Target: mpg
#> • Properties: -
#> • Features (10):
#> • dbl (10): am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt
#>
poinfo$predict(list(tsk("mtcars")))
#> $output
#>
#> ── <TaskRegr> (32x11): Motor Trends ────────────────────────────────────────────
#> • Target: mpg
#> • Properties: -
#> • Features (10):
#> • dbl (10): am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt
#>
# Specify customized console output for Task-objects
poinfo = po("info", log_target = "cat",
printer = list(Task = function(x) list(head_data = head(x$data()), nrow = nrow(x$data())))
)
poinfo$train(list(tsk("iris")))
#> Object passing through PipeOp info - Training
#>
#> $head_data
#> Species Petal.Length Petal.Width Sepal.Length Sepal.Width
#> <fctr> <num> <num> <num> <num>
#> 1: setosa 1.4 0.2 5.1 3.5
#> 2: setosa 1.4 0.2 4.9 3.0
#> 3: setosa 1.3 0.2 4.7 3.2
#> 4: setosa 1.5 0.2 4.6 3.1
#> 5: setosa 1.4 0.2 5.0 3.6
#> 6: setosa 1.7 0.4 5.4 3.9
#>
#> $nrow
#> [1] 150
#> $output
#>
#> ── <TaskClassif> (150x5): Iris Flowers ─────────────────────────────────────────
#> • Target: Species
#> • Target classes: setosa (33%), versicolor (33%), virginica (33%)
#> • Properties: multiclass
#> • Features (4):
#> • dbl (4): Petal.Length, Petal.Width, Sepal.Length, Sepal.Width
#>
poinfo$predict(list(tsk("iris")))
#> Object passing through PipeOp info - Prediction
#>
#> $head_data
#> Species Petal.Length Petal.Width Sepal.Length Sepal.Width
#> <fctr> <num> <num> <num> <num>
#> 1: setosa 1.4 0.2 5.1 3.5
#> 2: setosa 1.4 0.2 4.9 3.0
#> 3: setosa 1.3 0.2 4.7 3.2
#> 4: setosa 1.5 0.2 4.6 3.1
#> 5: setosa 1.4 0.2 5.0 3.6
#> 6: setosa 1.7 0.4 5.4 3.9
#>
#> $nrow
#> [1] 150
#> $output
#>
#> ── <TaskClassif> (150x5): Iris Flowers ─────────────────────────────────────────
#> • Target: Species
#> • Target classes: setosa (33%), versicolor (33%), virginica (33%)
#> • Properties: multiclass
#> • Features (4):
#> • dbl (4): Petal.Length, Petal.Width, Sepal.Length, Sepal.Width
#>