Package 'mllrnrs'

Title: R6-Based ML Learners for 'mlexperiments'
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.
Authors: Lorenz A. Kapsner [cre, aut, cph]
Maintainer: Lorenz A. Kapsner <[email protected]>
License: GPL (>= 3)
Version: 0.0.4
Built: 2024-11-02 04:44:04 UTC
Source: https://github.com/kapsner/mllrnrs

Help Index

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

Super class

mlexperiments::MLLearnerBase -> LearnerGlmnet

Methods

Public methods

Inherited methods

Method new()

Create a new LearnerGlmnet object.

Usage
LearnerGlmnet$new(metric_optimization_higher_better)
Arguments
metric_optimization_higher_better

A logical. Defines the direction of the optimization metric used throughout the hyperparameter optimization.

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
deep

Whether to make a deep clone.

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

Super class

mlexperiments::MLLearnerBase -> LearnerLightgbm

Methods

Public methods

Inherited methods

Method new()

Create a new LearnerLightgbm object.

Usage
LearnerLightgbm$new(metric_optimization_higher_better)
Arguments
metric_optimization_higher_better

A logical. Defines the direction of the optimization metric used throughout the hyperparameter optimization.

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
deep

Whether to make a deep clone.

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:

Super class

mlexperiments::MLLearnerBase -> LearnerRanger

Methods

Public methods

Inherited methods

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
deep

Whether to make a deep clone.

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

Super class

mlexperiments::MLLearnerBase -> LearnerXgboost

Methods

Public methods

Inherited methods

Method new()

Create a new LearnerXgboost object.

Usage
LearnerXgboost$new(metric_optimization_higher_better)
Arguments
metric_optimization_higher_better

A logical. Defines the direction of the optimization metric used throughout the hyperparameter optimization.

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
deep

Whether to make a deep clone.

See Also

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

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 = 2,
  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)