mllrnrs (version 0.0.5)

LearnerGlmnet: 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.

Super class

mlexperiments::MLLearnerBase -> LearnerGlmnet

Methods

Public methods

• LearnerGlmnet$new()

• LearnerGlmnet$clone()

Inherited methods

• mlexperiments::MLLearnerBase$bayesian_scoring_function()
• mlexperiments::MLLearnerBase$cross_validation()
• mlexperiments::MLLearnerBase$fit()
• mlexperiments::MLLearnerBase$predict()

---

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.

Details

Optimization metric: Can be used with

• mlexperiments::MLTuneParameters

• mlexperiments::MLCrossValidation

• mlexperiments::MLNestedCV

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)