Lrner: Lrner Class

In fuseMLR: Fusing Machine Learning in R

Lrner Class

Description

This class implements a learner. A Lrner object can only exist as a component of a TrainLayer or a TrainMetaLayer object.

Methods

Public methods

• Lrner$new()

• Lrner$print()

• Lrner$summary()

• Lrner$interface()

• Lrner$train()

• Lrner$getTrainLayer()

• Lrner$getNaRm()

• Lrner$getNaAction()

• Lrner$getId()

• Lrner$getPackage()

• Lrner$getIndSubset()

• Lrner$getVarSubset()

• Lrner$getParamPred()

• Lrner$getParamInterface()

• Lrner$getExtractPred()

---

Method new()

Initialize a default parameters list.

Usage

Lrner$new(
  id,
  package = NULL,
  lrn_fct,
  param_train_list,
  param_pred_list = list(),
  train_layer,
  na_action = "na.rm"
)

Arguments

id

character
Learner ID.

package

character
Package that implements the learn function. If NULL, the

lrn_fct

character
learn function is called from the current environment.

param_train_list

list
List of parameter for training.

param_pred_list

list
List of parameter for testing. Learn parameters.

train_layer

TrainLayer
Layer on which the learner is stored.

na_action

character
Handling of missing values. Set to "na.keep" to keep missing values, "na.rm" to remove individuals with missing values or "na.impute" (only applicable on meta-data) to impute missing values in meta-data. Only median and mode based imputations are actually handled. With the "na.keep" option, ensure that the provided learner can handle missing values.

---

Method print()

Printer

Usage

Lrner$print(...)

Arguments

...

any

---

Method summary()

Printer

Usage

Lrner$summary(...)

Arguments

...

any

---

Method interface()

Learner and prediction parameter interface. Use this function to provide how the following parameters are named in the learning function (lrn_fct) you provided when creating the learner, or in the predicting function.

Usage

Lrner$interface(
  x = "x",
  y = "y",
  object = "object",
  data = "data",
  extract_pred_fct = NULL
)

Arguments

x

character
Name of the argument to pass the matrix of independent variables in the original learning function.

y

character
Name of the argument to pass the response variable in the original learning function.

object

character
Name of the argument to pass the model in the original predicting function.

data

character
Name of the argument to pass new data in the original predicting function.

extract_pred_fct

character or function
If the predict function that is called for the model does not return a vector, then use this argument to specify a (or a name of a) function that can be used to extract vector of predictions. Default value is NULL, if predictions are in a vector.

---

Method train()

Tains the current learner (from class Lrner) on the current training data (from class TrainData).

Usage

Lrner$train(ind_subset = NULL, use_var_sel = FALSE, verbose = TRUE)

Arguments

ind_subset

vector
Individual ID subset on which the training will be performed.

use_var_sel

boolean
If TRUE, variable selection is performed before training.

verbose

boolean
Warning messages will be displayed if set to TRUE.

Returns

The resulting model, from class Model, is returned.

---

Method getTrainLayer()

The current layer is returned.

Usage

Lrner$getTrainLayer()

Returns

TrainLayer object.

---

Method getNaRm()

The current layer is returned.

Usage

Lrner$getNaRm()

---

Method getNaAction()

The current layer is returned.

Usage

Lrner$getNaAction()

---

Method getId()

Getter of the current learner ID.

Usage

Lrner$getId()

Returns

The current learner ID.

---

Method getPackage()

Getter of the learner package implementing the learn function.

Usage

Lrner$getPackage()

Returns

The name of the package implementing the learn function.

---

Method getIndSubset()

Getter of the learner package implementing the learn function.

Usage

Lrner$getIndSubset()

Returns

The name of the package implementing the learn function.

---

Method getVarSubset()

Getter of the variable subset used for training.

Usage

Lrner$getVarSubset()

Returns

The list of variables used for training is returned.

---

Method getParamPred()

Getter predicting parameter list.

Usage

Lrner$getParamPred()

Returns

The list of predicting parameters.

---

Method getParamInterface()

The current parameter interface is returned.

Usage

Lrner$getParamInterface()

Returns

A data.frame of interface.

---

Method getExtractPred()

The function to extract predicted values is returned.

Usage

Lrner$getExtractPred()

Returns

A data.frame of interface.

fuseMLR documentation built on April 3, 2025, 8:49 p.m.