PipeOpmissForest: PipeOpmissForest

In NADIA: NA Data Imputation Algorithms

PipeOpmissForest

Description

Implements missForest methods as mlr3 pipeline more about missForest autotune_missForest

Input and Output Channels

Input and output channels are inherited from PipeOpImpute.

Parameters

The parameters include inherited from ['PipeOpImpute'], as well as:

• id :: character(1)
  Identifier of resulting object, default "imput_missForest".

• cores :: integer(1)
  Number of threads used by parallel calculations. If NULL approximately half of available CPU cores will be used, default NULL.

• ntree_set :: integer(1)
  Vector with number of trees values for grid search, used only when optimize=TRUE, default c(100,200,500,1000).

• mtry_set :: integer(1)
  Vector with number of variables values randomly sampled at each split, used only when optimize=TRUE, default NULL.

• parallel :: logical(1)
  If TRUE parallel calculations are used, default FALSE.

• ntree :: integer(1)
  ntree from missForest function, default 100.

• optimize :: logical(1)
  If set TRUE, function will optimize parameters of imputation automatically. If parameters will be tuned by other method, should be set to FALSE, default FALSE.

• mtry :: integer(1)
  mtry from missForest function, default NULL.

• maxiter :: integer(1)
  maxiter from missForest function, default 20.

• maxnodes :: character(1)
  maxnodes from missForest function, default NULL

• out_fill :: character(1)
  Output log file location. If file already exists log message will be added. If NULL no log will be produced, default NULL.

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpImpute -> missForest_imputation

Methods

Public methods

• PipeOpmissForest$new()

• PipeOpmissForest$clone()

Inherited methods

• mlr3pipelines::PipeOp$help()
• mlr3pipelines::PipeOp$predict()
• mlr3pipelines::PipeOp$print()
• mlr3pipelines::PipeOp$train()

---

Method new()

Usage

PipeOpmissForest$new(
  id = "impute_missForest_B",
  cores = NULL,
  ntree_set = c(100, 200, 500, 1000),
  mtry_set = NULL,
  parallel = FALSE,
  mtry = NULL,
  ntree = 100,
  optimize = FALSE,
  maxiter = 20,
  maxnodes = NULL,
  out_file = NULL
)

---

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpmissForest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

  # Using debug learner for example purpose

  graph <- PipeOpmissForest$new() %>>% LearnerClassifDebug$new()
  graph_learner <- GraphLearner$new(graph)

  # Task with NA

  resample(tsk("pima"), graph_learner, rsmp("cv", folds = 3))

NADIA documentation built on Oct. 3, 2022, 1:05 a.m.