Package 'mllrnrs'

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

if (requireNamespace("glmnet", quietly = TRUE)) {
  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
if (requireNamespace("glmnet", quietly = TRUE) &&
requireNamespace("mlbench", quietly = TRUE) &&
requireNamespace("measures", quietly = TRUE)) {

  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$performance_metric_args <- list(positive = "1", negative = "0")
  glmnet_cv$performance_metric <- mlexperiments::metric("AUC")

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

  glmnet_cv$execute()
}

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

if (requireNamespace("glmnet", quietly = TRUE)) {
  LearnerGlmnet$new(metric_optimization_higher_better = FALSE)
}

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

if (requireNamespace("lightgbm", quietly = TRUE)) {
  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
if (requireNamespace("lightgbm", quietly = TRUE) &&
requireNamespace("mlbench", quietly = TRUE) &&
requireNamespace("measures", quietly = TRUE)) {

  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", negative = "0")
  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`
## ------------------------------------------------

if (requireNamespace("lightgbm", quietly = TRUE)) {
  LearnerLightgbm$new(metric_optimization_higher_better = FALSE)
}

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

if (requireNamespace("ranger", quietly = TRUE)) {
  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
if (requireNamespace("ranger", quietly = TRUE) &&
requireNamespace("mlbench", quietly = TRUE) &&
requireNamespace("measures", quietly = TRUE)) {

  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", negative = "0")
  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`
## ------------------------------------------------

if (requireNamespace("ranger", quietly = TRUE)) {
  LearnerRanger$new()
}

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

if (requireNamespace("xgboost", quietly = TRUE)) {
  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

if (requireNamespace("xgboost", quietly = TRUE) &&
requireNamespace("mlbench", quietly = TRUE) &&
requireNamespace("measures", quietly = TRUE)) {

  # 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", negative = "0")
  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`
## ------------------------------------------------

if (requireNamespace("xgboost", quietly = TRUE)) {
  LearnerXgboost$new(metric_optimization_higher_better = FALSE)
}