PipeOpImpute: Imputation Base Class
In mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'

PipeOpImpute

R Documentation

Imputation Base Class

Description

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, empty_level_control = 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$initialize(). Default list().
whole_task_dependent :: logical(1)
Whether the context_columns parameter should be added which lets the user limit the columns that are used for imputation inference. This should generally be FALSE if imputation depends only on individual features (e.g. mode imputation), and TRUE if imputation depends on other features as well (e.g. kNN-imputation).
empty_level_control :: logical(1)
Control how to handle edge cases where NAs occur in factor or ordered features only during prediction but not during training. Can be one of "never", "always", or "param":
- If set to "never", no empty level is introduced during training, but columns that have missing values only during prediction will not be imputed.
- If set to "always", an unseen level is added to the feature during training and missing values are imputed as that value during prediction.
- Finally, if set to "param", the hyperparameter create_empty_level is added and control over this behavior is left to the user.
For implementation details, see Internals below. Default is "never".
packages :: character
Set of all required packages for the PipeOp's private$.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.
create_empty_level :: logical(1)
Whether an empty level should always be created for factor or ordered columns during training. If FALSE, columns that had no NAs during training but have NAs during prediction will not be imputed. This parameter is only present if the constructor is called with the empty_level_control argument set to "param". Initialized to FALSE.

Internals

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

Internally, the construction argument empty_level_control and the hyperparameter create_empty_level (should it exist) modify the private$.create_empty_level field. Behavior then depends on whether this field is set to TRUE or FALSE and works by controlling for which cases imputation is performed on factor or ordered columns. Its setting has no impact on columns of other types.
If private$.create_empty_level is set to TRUE, private$.impute() is called for all factor or ordered columns during training, regardless of whether they have any missing values. For this to lead to the creation of an empty level for columns with no missing values, inheriting PipeOps must implement private$.train_imputer() in such a way that it returns the name of the level to be created for the feature types factor and ordered.
If private$.create_empty_level is set to FALSE, private$.impute() is not called during prediction for factor or ordered columns which were not modified during training. This means that NAs will not be imputed for these columns.
See PipeOpImputeOOR, for a detailed explanation of why these controls are necessary.

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. If model has length zero, feature is returned unchanged.

Other PipeOps: PipeOp, PipeOpEncodePL, 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_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_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