Imputation Base Class

Abstract base class for feature imputation.

Format

Abstract R6Class object inheriting from PipeOp.

Construction

PipeOpImpute$$new(id, param_set = ps(), param_vals = list(), whole_task_dependent = FALSE, packages = character(0), task_type = "Task")

• id :: character(1)
  Identifier of resulting object. See $id slot of PipeOp.

• param_set :: ParamSet
  Parameter space description. This should be created by the subclass and given to super$initialize().

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings given in param_set. The subclass should have its own param_vals parameter and pass it on to super$initialize(). Default list().

• whole_task_dependent :: logical(1)
  Whether the context_columns parameter should be added which lets the user limit the columns that are used for imputation inference. This should generally be FALSE if imputation depends only on individual features (e.g. mode imputation), and TRUE if imputation depends on other features as well (e.g. kNN-imputation).

• packages :: character
  Set of all required packages for the PipeOp's private$.train and private$.predict methods. See $packages slot. Default is character(0).

• task_type :: character(1)
  The class of Task that should be accepted as input and will be returned as output. This should generally be a character(1) identifying a type of Task, e.g. "Task", "TaskClassif" or "TaskRegr" (or another subclass introduced by other packages). Default is "Task".

• feature_types :: character
  Feature types affected by the PipeOp. See private$.select_cols() for more information.

Input and Output Channels

PipeOpImpute has one input channel named "input", taking a Task, or a subclass of Task if the task_type construction argument is given as such; both during training and prediction.

PipeOpImpute has one output channel named "output", producing a Task, or a subclass; the Task type is the same as for input; both during training and prediction.

The output Task is the modified input Task with features imputed according to the private$.impute() function.

State

The $state is a named list; besides members added by inheriting classes, the members are:

• affected_cols :: character
  Names of features being selected by the affect_columns parameter.

• context_cols :: character
  Names of features being selected by the context_columns parameter.

• intasklayout :: data.table
  Copy of the training Task's $feature_types slot. This is used during prediction to ensure that the prediction Task has the same features, feature layout, and feature types as during training.

• outtasklayout :: data.table
  Copy of the trained Task's $feature_types slot. This is used during prediction to ensure that the Task resulting from the prediction operation has the same features, feature layout, and feature types as after training.

• model :: named list
  Model used for imputation. This is a list named by Task features, containing the result of the private$.train_imputer() or private$.train_nullmodel() function for each one.

• imputed_train :: character
  Names of features that were imputed during training. This is used to ensure that factor levels that were added during training are also added during prediction. Note that features that are imputed during prediction but not during training will still have inconsistent factor levels.

Parameters

• affect_columns :: function | Selector | NULL
  What columns the PipeOpImpute should operate on. The parameter must be a Selector function, which takes a Task as argument and returns a character of features to use.
  See Selector for example functions. Defaults to NULL, which selects all features.

• context_columns :: function | Selector | NULL
  What columns the PipeOpImpute imputation may depend on. This parameter is only present if the constructor is called with the whole_task_dependent argument set to TRUE.
  The parameter must be a Selector function, which takes a Task as argument and returns a character of features to use.
  See Selector for example functions. Defaults to NULL, which selects all features.

Internals

PipeOpImpute is an abstract class inheriting from PipeOp that makes implementing imputer PipeOps simple.

Fields

Fields inherited from PipeOp.

Methods

Methods inherited from PipeOp, as well as:

• .select_cols(task)
  (Task) -> character
  Selects which columns the PipeOp operates on. In contrast to the affect_columns parameter. private$.select_cols() is for the inheriting class to determine which columns the operator should function on, e.g. based on feature type, while affect_columns is a way for the user to limit the columns that a PipeOpTaskPreproc should operate on. This method can optionally be overloaded when inheriting PipeOpImpute; If this method is not overloaded, it defaults to selecting the columns of type indicated by the feature_types construction argument.

• .train_imputer(feature, type, context)
  (atomic, character(1), data.table) -> any
  Abstract function that must be overloaded when inheriting. Called once for each feature selected by affect_columns to create the model entry to be used for private$.impute(). This function is only called for features with at least one non-missing value.

• .train_nullmodel(feature, type, context)
  (atomic, character(1), data.table) -> any
  Like .train_imputer(), but only called for each feature that only contains missing values. This is not an abstract function and, if not overloaded, gives a default response of 0 (integer, numeric), c(TRUE, FALSE) (logical), all available levels (factor/ordered), or the empty string (character).

• .impute(feature, type, model, context)
  (atomic, character(1), any, data.table) -> atomic
  Imputes the features. model is the model created by private$.train_imputer() Default behaviour is to assume model is an atomic vector from which values are sampled to impute missing values of feature. model may have an attribute probabilities for non-uniform sampling.

See also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEnsemble, 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_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 Imputation PipeOps: mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample