Abstract base class for feature imputation.

Format

Abstract R6Class object inheriting from PipeOp.

Construction

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".

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 $impute() function.

State

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.

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. 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()

See also