Wrap a Learner into a PipeOp to to predict multiple Quantiles

Wraps a LearnerRegr into a PipeOp to predict multiple quantiles.

PipeOpLearnerQuantiles only supports LearnerRegrs that have quantiles as a possible pedict_type.

It produces quantile-based predictions for multiple quantiles in one PredictionRegr. This is especially helpful if the LearnerRegr can only predict one quantile (like for example LearnerRegrGBM in mlr3extralearners)

Inherits the $param_set (and therefore $param_set$values) from the Learner it is constructed from.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpLearnerQuantiles$new(learner, id = NULL, param_vals = list())

• learner :: Learner | character(1)
Learner to wrap, or a string identifying a Learner in the mlr3::mlr_learners Dictionary. The Learner has to be a LearnerRegr with predict_type "quantiles". This argument is always cloned; to access the Learner inside PipeOpLearnerQuantiles by-reference, use $learner.

• id :: character(1) Identifier of the resulting object, internally defaulting to the id of the Learner being wrapped.

• 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

PipeOpLearnerQuantiles has one input channel named "input", taking a TaskRegr specific to the Learner type given to learner during construction; both during training and prediction.

PipeOpLearnerQuantiles has one output channel named "output", producing NULL during training and a PredictionRegr object during prediction.

The output during prediction is a PredictionRegr on the prediction input data that aggregates all results produced by the Learner for each quantile in quantiles. trained on the training input data.

State

The $state is set during training. It is a named list with the member:

• model_states :: list
  List of the states of all models created by the Learner's $.train() function.

Parameters

The parameters are exactly the parameters of the Learner wrapped by this object.

• q_vals :: numeric
  Quantiles to use for training and prediction. Initialized to c(0.05, 0.5, 0.95)

• q_response :: numeric(1)
  Which quantile in quantiles to use as a response for the PredictionRegr during prediction. Initialized to 0.5.

Internals

The $state is updated during training.

Fields

Fields inherited from PipeOp, as well as:

• learner :: LearnerRegr
Learner that is being wrapped. Read-only.

• learner_model :: Learner
  If PipeOpLearnerQuantiles has been trained, this is a list containing the Learners for each quantile. Otherwise, this contains the Learner that is being wrapped. Read-only.

• predict_type :: character(1)
  Predict type of the PipeOpLearnerQuantiles, which is always "response" "quantiles".

Methods

Methods inherited from PipeOp.

See also

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_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_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_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 Meta PipeOps: mlr_pipeops_learner, mlr_pipeops_learner_cv, mlr_pipeops_learner_pi_cvplus

Examples

library("mlr3")

task = tsk("boston_housing")
learner = lrn("regr.debug")
po = mlr_pipeops$get("learner_quantiles", learner)

po$train(list(task))
#> $output
#> NULL
#> 
po$predict(list(task))
#> $output
#> <PredictionRegr> for 506 observations:
#>  row_ids truth q0.05 q0.5 q0.95 response
#>        1  24.0  10.2 21.2  43.4     21.2
#>        2  21.6  10.2 21.2  43.4     21.2
#>        3  34.7  10.2 21.2  43.4     21.2
#>      ---   ---   ---  ---   ---      ---
#>      504  23.9  10.2 21.2  43.4     21.2
#>      505  22.0  10.2 21.2  43.4     21.2
#>      506  19.0  10.2 21.2  43.4     21.2
#>