Title:	R6-Based ML Learners for 'mlexperiments'
Version:	0.0.5
Description:	Enhances 'mlexperiments' https://CRAN.R-project.org/package=mlexperiments with additional machine learning ('ML') learners. The package provides R6-based learners for the following algorithms: 'glmnet' https://CRAN.R-project.org/package=glmnet, 'ranger' https://CRAN.R-project.org/package=ranger, 'xgboost' https://CRAN.R-project.org/package=xgboost, and 'lightgbm' https://CRAN.R-project.org/package=lightgbm. These can be used directly with the 'mlexperiments' R package.
License:	GPL (≥ 3)
URL:	https://github.com/kapsner/mllrnrs
BugReports:	https://github.com/kapsner/mllrnrs/issues
Depends:	R (≥ 4.1.0)
Imports:	data.table, kdry, mlexperiments, R6, stats
Suggests:	glmnet, lightgbm (≥ 4.0.0), lintr, mlbench, mlr3measures, ParBayesianOptimization, quarto, ranger, splitTools, testthat (≥ 3.0.1), xgboost
VignetteBuilder:	quarto
Config/testthat/edition:	3
Config/testthat/parallel:	false
Date/Publication:	2025-03-05 15:00:02 UTC
Encoding:	UTF-8
SystemRequirements:	Quarto command line tools (https://github.com/quarto-dev/quarto-cli).
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-03-05 14:22:49 UTC; user
Author:	Lorenz A. Kapsner [cre, aut, cph]
Maintainer:	Lorenz A. Kapsner <lorenz.kapsner@gmail.com>
Repository:	CRAN

R6 Class to construct a Glmnet learner

Description

The LearnerGlmnet class is the interface to the glmnet R package for use with the mlexperiments package.

Details

Optimization metric: Can be used with

• mlexperiments::MLTuneParameters

• mlexperiments::MLCrossValidation

• mlexperiments::MLNestedCV

Super class

mlexperiments::MLLearnerBase -> LearnerGlmnet

Methods

Public methods

• LearnerGlmnet$new()

• LearnerGlmnet$clone()

Method new()

Create a new LearnerGlmnet object.

Usage

LearnerGlmnet$new(metric_optimization_higher_better)

Arguments

Returns

A new LearnerGlmnet R6 object.

Examples

LearnerGlmnet$new(metric_optimization_higher_better = FALSE)

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerGlmnet$clone(deep = FALSE)

Arguments

See Also

glmnet::glmnet(), glmnet::cv.glmnet()

Examples

# binary classification

library(mlbench)
data("PimaIndiansDiabetes2")
dataset <- PimaIndiansDiabetes2 |>
  data.table::as.data.table() |>
  na.omit()

seed <- 123
feature_cols <- colnames(dataset)[1:8]

train_x <- model.matrix(
  ~ -1 + .,
  dataset[, .SD, .SDcols = feature_cols]
)
train_y <- as.integer(dataset[, get("diabetes")]) - 1L

fold_list <- splitTools::create_folds(
  y = train_y,
  k = 3,
  type = "stratified",
  seed = seed
)
glmnet_cv <- mlexperiments::MLCrossValidation$new(
  learner = mllrnrs::LearnerGlmnet$new(
    metric_optimization_higher_better = FALSE
  ),
  fold_list = fold_list,
  ncores = 2,
  seed = 123
)
glmnet_cv$learner_args <- list(
  alpha = 1,
  lambda = 0.1,
  family = "binomial",
  type.measure = "class",
  standardize = TRUE
)
glmnet_cv$predict_args <- list(type = "response")
glmnet_cv$performance_metric_args <- list(positive = "1")
glmnet_cv$performance_metric <- mlexperiments::metric("auc")

# set data
glmnet_cv$set_data(
  x = train_x,
  y = train_y
)

glmnet_cv$execute()

## ------------------------------------------------
## Method `LearnerGlmnet$new`
## ------------------------------------------------

LearnerGlmnet$new(metric_optimization_higher_better = FALSE)

R6 Class to construct a LightGBM learner

Description

The LearnerLightgbm class is the interface to the lightgbm R package for use with the mlexperiments package.

Details

Optimization metric: needs to be specified with the learner parameter metric. The following options can be set via options():

• "mlexperiments.optim.lgb.nrounds" (default: 5000L)

• "mlexperiments.optim.lgb.early_stopping_rounds" (default: 500L)

• "mlexperiments.lgb.print_every_n" (default: 50L)

• "mlexperiments.lgb.verbose" (default: -1L)

LearnerLightgbm can be used with

• mlexperiments::MLTuneParameters

• mlexperiments::MLCrossValidation

• mlexperiments::MLNestedCV

Super class

mlexperiments::MLLearnerBase -> LearnerLightgbm

Methods

Public methods

• LearnerLightgbm$new()

• LearnerLightgbm$clone()

Method new()

Create a new LearnerLightgbm object.

Usage

LearnerLightgbm$new(metric_optimization_higher_better)

Arguments

Returns

A new LearnerLightgbm R6 object.

Examples

LearnerLightgbm$new(metric_optimization_higher_better = FALSE)

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerLightgbm$clone(deep = FALSE)

Arguments

See Also

lightgbm::lgb.train(), lightgbm::lgb.cv()

Examples

# binary classification

library(mlbench)
data("PimaIndiansDiabetes2")
dataset <- PimaIndiansDiabetes2 |>
  data.table::as.data.table() |>
  na.omit()

seed <- 123
feature_cols <- colnames(dataset)[1:8]

param_list_lightgbm <- expand.grid(
  bagging_fraction = seq(0.6, 1, .2),
  feature_fraction = seq(0.6, 1, .2),
  min_data_in_leaf = seq(10, 50, 10),
  learning_rate = seq(0.1, 0.2, 0.1),
  num_leaves = seq(10, 50, 10),
  max_depth = -1L
)

train_x <- model.matrix(
  ~ -1 + .,
  dataset[, .SD, .SDcols = feature_cols]
)
train_y <- as.integer(dataset[, get("diabetes")]) - 1L

fold_list <- splitTools::create_folds(
  y = train_y,
  k = 3,
  type = "stratified",
  seed = seed
)
lightgbm_cv <- mlexperiments::MLCrossValidation$new(
  learner = mllrnrs::LearnerLightgbm$new(
    metric_optimization_higher_better = FALSE
  ),
  fold_list = fold_list,
  ncores = 2,
  seed = 123
)
lightgbm_cv$learner_args <- c(
  as.list(
    data.table::data.table(
      param_list_lightgbm[37, ],
      stringsAsFactors = FALSE
    ),
  ),
  list(
    objective = "binary",
    metric = "binary_logloss"
  ),
  nrounds = 45L
)
lightgbm_cv$performance_metric_args <- list(positive = "1")
lightgbm_cv$performance_metric <- mlexperiments::metric("auc")

# set data
lightgbm_cv$set_data(
  x = train_x,
  y = train_y
)

lightgbm_cv$execute()


## ------------------------------------------------
## Method `LearnerLightgbm$new`
## ------------------------------------------------

LearnerLightgbm$new(metric_optimization_higher_better = FALSE)

R6 Class to construct a Ranger learner

Description

The LearnerRanger class is the interface to the ranger R package for use with the mlexperiments package.

Details

Optimization metric:

• classification: classification error rate

• regression: mean squared error Can be used with

• mlexperiments::MLTuneParameters

• mlexperiments::MLCrossValidation

• mlexperiments::MLNestedCV

Super class

mlexperiments::MLLearnerBase -> LearnerRanger

Methods

Public methods

• LearnerRanger$new()

• LearnerRanger$clone()

Method new()

Create a new LearnerRanger object.

Usage

LearnerRanger$new()

Returns

A new LearnerRanger R6 object.

Examples

LearnerRanger$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRanger$clone(deep = FALSE)

Arguments

See Also

ranger::ranger()

Examples

# binary classification

library(mlbench)
data("PimaIndiansDiabetes2")
dataset <- PimaIndiansDiabetes2 |>
  data.table::as.data.table() |>
  na.omit()

seed <- 123
feature_cols <- colnames(dataset)[1:8]

param_list_ranger <- expand.grid(
  num.trees = seq(500, 1000, 500),
  mtry = seq(2, 6, 2),
  min.node.size = seq(1, 9, 4),
  max.depth = seq(1, 9, 4),
  sample.fraction = seq(0.5, 0.8, 0.3)
)

train_x <- model.matrix(
  ~ -1 + .,
  dataset[, .SD, .SDcols = feature_cols]
)
train_y <- as.integer(dataset[, get("diabetes")]) - 1L

fold_list <- splitTools::create_folds(
  y = train_y,
  k = 3,
  type = "stratified",
  seed = seed
)
ranger_cv <- mlexperiments::MLCrossValidation$new(
  learner = mllrnrs::LearnerRanger$new(),
  fold_list = fold_list,
  ncores = 2,
  seed = 123
)
ranger_cv$learner_args <- c(
  as.list(
    data.table::data.table(
      param_list_ranger[37, ],
      stringsAsFactors = FALSE
    ),
  ),
  list(classification = TRUE)
)
ranger_cv$performance_metric_args <- list(positive = "1")
ranger_cv$performance_metric <- mlexperiments::metric("auc")

# set data
ranger_cv$set_data(
  x = train_x,
  y = train_y
)

ranger_cv$execute()


## ------------------------------------------------
## Method `LearnerRanger$new`
## ------------------------------------------------

LearnerRanger$new()

R6 Class to construct a Xgboost learner

Description

The LearnerXgboost class is the interface to the xgboost R package for use with the mlexperiments package.

Details

Optimization metric: needs to be specified with the learner parameter eval_metric. The following options can be set via options():

• "mlexperiments.optim.xgb.nrounds" (default: 5000L)

• "mlexperiments.optim.xgb.early_stopping_rounds" (default: 500L)

• "mlexperiments.xgb.print_every_n" (default: 50L)

• "mlexperiments.xgb.verbose" (default: FALSE)

LearnerXgboost can be used with

• mlexperiments::MLTuneParameters

• mlexperiments::MLCrossValidation

• mlexperiments::MLNestedCV

Super class

mlexperiments::MLLearnerBase -> LearnerXgboost

Methods

Public methods

• LearnerXgboost$new()

• LearnerXgboost$clone()

Method new()

Create a new LearnerXgboost object.

Usage

LearnerXgboost$new(metric_optimization_higher_better)

Arguments

Returns

A new LearnerXgboost R6 object.

Examples

LearnerXgboost$new(metric_optimization_higher_better = FALSE)

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerXgboost$clone(deep = FALSE)

Arguments

See Also

xgboost::xgb.train(), xgboost::xgb.cv()

Examples

# binary classification
Sys.setenv("OMP_THREAD_LIMIT" = 2)

library(mlbench)
data("PimaIndiansDiabetes2")
dataset <- PimaIndiansDiabetes2 |>
  data.table::as.data.table() |>
  na.omit()

seed <- 123
feature_cols <- colnames(dataset)[1:8]

param_list_xgboost <- expand.grid(
   subsample = seq(0.6, 1, .2),
   colsample_bytree = seq(0.6, 1, .2),
   min_child_weight = seq(1, 5, 4),
   learning_rate = seq(0.1, 0.2, 0.1),
   max_depth = seq(1, 5, 4),
   nthread = 2
)

train_x <- model.matrix(
  ~ -1 + .,
  dataset[, .SD, .SDcols = feature_cols]
)
train_y <- as.integer(dataset[, get("diabetes")]) - 1L

fold_list <- splitTools::create_folds(
  y = train_y,
  k = 3,
  type = "stratified",
  seed = seed
)
xgboost_cv <- mlexperiments::MLCrossValidation$new(
  learner = mllrnrs::LearnerXgboost$new(
    metric_optimization_higher_better = FALSE
  ),
  fold_list = fold_list,
  ncores = 2L,
  seed = 123
)
xgboost_cv$learner_args <- c(
  as.list(
    data.table::data.table(
      param_list_xgboost[37, ],
      stringsAsFactors = FALSE
    ),
  ),
  list(
    objective = "binary:logistic",
    eval_metric = "logloss"
  ),
  nrounds = 45L
)
xgboost_cv$performance_metric_args <- list(positive = "1")
xgboost_cv$performance_metric <- mlexperiments::metric("auc")

# set data
xgboost_cv$set_data(
  x = train_x,
  y = train_y
)

xgboost_cv$execute()


## ------------------------------------------------
## Method `LearnerXgboost$new`
## ------------------------------------------------

LearnerXgboost$new(metric_optimization_higher_better = FALSE)