Abstract base class for feature imputation.
PipeOpImpute$$new(id, param_set = ParamSet$new(), 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 $train and $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".
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 $impute() function.
The $state is a named list; besides members added by inheriting classes, the members are:
affect_cols :: character
Names of features being selected by the affect_columns parameter.
inference_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 $train_imputer() function for each one.
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.
PipeOpImpute is an abstract class inheriting from PipeOp that makes implementing imputer PipeOps simple.
Fields inherited from PipeOp.
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. 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.
train_imputer(feature, type, context)
(atomic, character(1), data.table) -> any
Called once for each feature selected by affect_columns to create the model entry to be used for $impute().
impute(feature, type, model, context)
(atomic, character(1), any, data.table) -> atomic
Imputes the features. model is the model created by $train_imputer()
Other PipeOps:
PipeOpEnsemble,
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_copy,
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_imputehist,
mlr_pipeops_imputemean,
mlr_pipeops_imputemedian,
mlr_pipeops_imputenewlvl,
mlr_pipeops_imputesample,
mlr_pipeops_kernelpca,
mlr_pipeops_learner,
mlr_pipeops_missind,
mlr_pipeops_modelmatrix,
mlr_pipeops_mutate,
mlr_pipeops_nop,
mlr_pipeops_pca,
mlr_pipeops_quantilebin,
mlr_pipeops_regravg,
mlr_pipeops_removeconstants,
mlr_pipeops_scalemaxabs,
mlr_pipeops_scalerange,
mlr_pipeops_scale,
mlr_pipeops_select,
mlr_pipeops_smote,
mlr_pipeops_spatialsign,
mlr_pipeops_subsample,
mlr_pipeops_unbranch,
mlr_pipeops_yeojohnson,
mlr_pipeops
Other Imputation PipeOps:
mlr_pipeops_imputehist,
mlr_pipeops_imputemean,
mlr_pipeops_imputemedian,
mlr_pipeops_imputenewlvl,
mlr_pipeops_imputesample