mlr3pipelines (version 0.3.0)

mlr_pipeops_learner: PipeOpLearner

Description

Wraps an mlr3::Learner into a PipeOp.

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

Using PipeOpLearner, it is possible to embed mlr3::Learners into Graphs, which themselves can be turned into Learners using GraphLearner. This way, preprocessing and ensemble methods can be included into a machine learning pipeline which then can be handled as singular object for resampling, benchmarking and tuning.

Format

R6Class object inheriting from PipeOp.

Construction

PipeOpLearner$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.

• 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

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

PipeOpLearner has one output channel named "output", producing NULL during training and a Prediction subclass during prediction; this subclass is specific to the Learner type given to learner during construction.

The output during prediction is the Prediction on the prediction input data, produced by the Learner trained on the training input data.

State

The $state is set to the $state slot of the Learner object. It is a named list with members:

• model :: any Model created by the Learner's $.train() function.

• train_log :: data.table with columns class (character), msg (character) Errors logged during training.

• train_time :: numeric(1) Training time, in seconds.

• predict_log :: NULL | data.table with columns class (character), msg (character) Errors logged during prediction.

• predict_time :: NULL | numeric(1) Prediction time, in seconds.

Parameters

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

Internals

The $state is currently not updated by prediction, so the $state$predict_log and $state$predict_time will always be NULL.

Fields

Fields inherited from PipeOp, as well as:

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

• learner_model :: Learner Learner that is being wrapped. This learner contains the model if the PipeOp is trained. Read-only.

Methods

Methods inherited from PipeOp.

See Also

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_colapply, 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_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

Other Meta PipeOps: mlr_pipeops_learner_cv

Examples

# NOT RUN {
library("mlr3")

task = tsk("iris")
learner = lrn("classif.rpart", cp = 0.1)
lrn_po = mlr_pipeops$get("learner", learner)

lrn_po$train(list(task))
lrn_po$predict(list(task))
# }