lightgbm: Multiclass Classification

    # nolint start
    library(mlexperiments)
    library(mllrnrs)

See https://github.com/kapsner/mllrnrs/blob/main/R/learner_lightgbm.R for implementation details.

# Preprocessing

## Import and Prepare Data

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

    feature_cols <- colnames(dataset)[1:180]
    target_col <- "Class"

## General Configurations

    seed <- 123
    if (isTRUE(as.logical(Sys.getenv("_R_CHECK_LIMIT_CORES_")))) {
      # on cran
      ncores <- 2L
    } else {
      ncores <- ifelse(
        test = parallel::detectCores() > 4,
        yes = 4L,
        no = ifelse(
          test = parallel::detectCores() < 2L,
          yes = 1L,
          no = parallel::detectCores()
        )
      )
    }
    options("mlexperiments.bayesian.max_init" = 4L)
    options("mlexperiments.optim.lgb.nrounds" = 20L)
    options("mlexperiments.optim.lgb.early_stopping_rounds" = 5L)

## Generate Training- and Test Data

    data_split <- splitTools::partition(
      y = dataset[, get(target_col)],
      p = c(train = 0.7, test = 0.3),
      type = "stratified",
      seed = seed
    )

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

    test_x <- model.matrix(
      ~ -1 + .,
      dataset[data_split$test, .SD, .SDcols = feature_cols]
    )
    test_y <- as.integer(dataset[data_split$test, get(target_col)]) - 1L

## Generate Training Data Folds

    fold_list <- splitTools::create_folds(
      y = train_y,
      k = 3,
      type = "stratified",
      seed = seed
    )

# Experiments

## Prepare Experiments

    # required learner arguments, not optimized
    learner_args <- list(
      max_depth = -1L,
      verbose = -1L,
      objective = "multiclass",
      metric = "multi_logloss",
      num_class = "3"
    )

    # set arguments for predict function and performance metric,
    # required for mlexperiments::MLCrossValidation and
    # mlexperiments::MLNestedCV
    predict_args <- list(reshape = TRUE)
    performance_metric <- metric("ACC")
    performance_metric_args <- NULL
    return_models <- FALSE

    # required for grid search and initialization of bayesian optimization
    parameter_grid <- expand.grid(
      bagging_fraction = seq(0.6, 1, .2),
      feature_fraction = seq(0.6, 1, .2),
      min_data_in_leaf = seq(2, 10, 2),
      learning_rate = seq(0.1, 0.2, 0.1),
      num_leaves = seq(2, 20, 4)
    )
    # reduce to a maximum of 10 rows
    if (nrow(parameter_grid) > 10) {
      set.seed(123)
      sample_rows <- sample(seq_len(nrow(parameter_grid)), 10, FALSE)
      parameter_grid <- kdry::mlh_subset(parameter_grid, sample_rows)
    }

    # required for bayesian optimization
    parameter_bounds <- list(
      bagging_fraction = c(0.2, 1),
      feature_fraction = c(0.2, 1),
      min_data_in_leaf = c(2L, 12L),
      learning_rate = c(0.1, 0.2),
      num_leaves =  c(2L, 20L)
    )
    optim_args <- list(
      n_iter = ncores,
      kappa = 3.5,
      acq = "ucb"
    )

## Hyperparameter Tuning

### Grid Search

    tuner <- mlexperiments::MLTuneParameters$new(
      learner = mllrnrs::LearnerLightgbm$new(
        metric_optimization_higher_better = FALSE
      ),
      strategy = "grid",
      ncores = ncores,
      seed = seed
    )

    tuner$parameter_grid <- parameter_grid
    tuner$learner_args <- learner_args
    tuner$split_type <- "stratified"

    tuner$set_data(
      x = train_x,
      y = train_y
    )

    tuner_results_grid <- tuner$execute(k = 3)
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -0.656204
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #>
    #> Parameter settings [==================>-----------------------------------------------------------------------------] 2/10 ( 20%)
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -0.656204
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #>
    #> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -0.656204
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #>
    #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -0.656204
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #>
    #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -0.656204
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #>
    #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -0.656204
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #>
    #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -0.656204
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #>
    #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -0.656204
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #>
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -0.656204
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #>
    #> Parameter settings [===============================================================================================] 10/10 (100%)
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -1.424655
    #> [LightGBM] [Info] Start training from score -0.656204
    #> [LightGBM] [Info] Start training from score -1.422637
    #> [LightGBM] [Info] Start training from score -1.428239
    #> [LightGBM] [Info] Start training from score -0.655482

    head(tuner_results_grid)
    #>    setting_id metric_optim_mean nrounds bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves max_depth
    #> 1:          1         0.1353093      33              0.6              0.6                4           0.2         18        -1
    #> 2:          2         0.1282925      59              0.8              1.0               10           0.2          6        -1
    #> 3:          3         0.2360723     100              0.8              0.8                4           0.1          2        -1
    #> 4:          4         0.1298904      71              1.0              0.8                4           0.1         10        -1
    #> 5:          5         0.1357692      32              1.0              0.6                6           0.2         18        -1
    #> 6:          6         0.1313455      64              1.0              1.0                8           0.1         14        -1
    #>    verbose  objective        metric num_class
    #> 1:      -1 multiclass multi_logloss         3
    #> 2:      -1 multiclass multi_logloss         3
    #> 3:      -1 multiclass multi_logloss         3
    #> 4:      -1 multiclass multi_logloss         3
    #> 5:      -1 multiclass multi_logloss         3
    #> 6:      -1 multiclass multi_logloss         3

### Bayesian Optimization

    tuner <- mlexperiments::MLTuneParameters$new(
      learner = mllrnrs::LearnerLightgbm$new(
        metric_optimization_higher_better = FALSE
      ),
      strategy = "bayesian",
      ncores = ncores,
      seed = seed
    )

    tuner$parameter_grid <- parameter_grid
    tuner$parameter_bounds <- parameter_bounds

    tuner$learner_args <- learner_args
    tuner$optim_args <- optim_args

    tuner$split_type <- "stratified"

    tuner$set_data(
      x = train_x,
      y = train_y
    )

    tuner_results_bayesian <- tuner$execute(k = 3)
    #>
    #> Registering parallel backend using 4 cores.

    head(tuner_results_bayesian)
    #>    Epoch setting_id bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves gpUtility acqOptimum inBounds
    #> 1:     0          1              0.6              0.6                4           0.2         18        NA      FALSE     TRUE
    #> 2:     0          2              0.8              1.0               10           0.2          6        NA      FALSE     TRUE
    #> 3:     0          3              0.8              0.8                4           0.1          2        NA      FALSE     TRUE
    #> 4:     0          4              1.0              0.8                4           0.1         10        NA      FALSE     TRUE
    #> 5:     0          5              1.0              0.6                6           0.2         18        NA      FALSE     TRUE
    #> 6:     0          6              1.0              1.0                8           0.1         14        NA      FALSE     TRUE
    #>    Elapsed      Score metric_optim_mean nrounds errorMessage max_depth verbose  objective        metric num_class
    #> 1:   1.283 -0.1353093         0.1353093      33           NA        -1      -1 multiclass multi_logloss         3
    #> 2:   1.300 -0.1282925         0.1282925      59           NA        -1      -1 multiclass multi_logloss         3
    #> 3:   1.277 -0.2360723         0.2360723     100           NA        -1      -1 multiclass multi_logloss         3
    #> 4:   1.460 -0.1298904         0.1298904      71           NA        -1      -1 multiclass multi_logloss         3
    #> 5:   0.360 -0.1357692         0.1357692      32           NA        -1      -1 multiclass multi_logloss         3
    #> 6:   0.561 -0.1313455         0.1313455      64           NA        -1      -1 multiclass multi_logloss         3

## k-Fold Cross Validation

    validator <- mlexperiments::MLCrossValidation$new(
      learner = mllrnrs::LearnerLightgbm$new(
        metric_optimization_higher_better = FALSE
      ),
      fold_list = fold_list,
      ncores = ncores,
      seed = seed
    )

    validator$learner_args <- tuner$results$best.setting[-1]

    validator$predict_args <- predict_args
    validator$performance_metric <- performance_metric
    validator$performance_metric_args <- performance_metric_args
    validator$return_models <- return_models

    validator$set_data(
      x = train_x,
      y = train_y
    )

    validator_results <- validator$execute()
    #>
    #> CV fold: Fold1
    #>
    #> CV fold: Fold2
    #> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
    #>
    #> CV fold: Fold3
    #> CV progress [========================================================================================================] 3/3 (100%)
    #>

    head(validator_results)
    #>     fold performance bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves nrounds max_depth verbose
    #> 1: Fold1   0.9663978              0.8              0.6                8           0.1         14      66        -1      -1
    #> 2: Fold2   0.9541161              0.8              0.6                8           0.1         14      66        -1      -1
    #> 3: Fold3   0.9610215              0.8              0.6                8           0.1         14      66        -1      -1
    #>     objective        metric num_class
    #> 1: multiclass multi_logloss         3
    #> 2: multiclass multi_logloss         3
    #> 3: multiclass multi_logloss         3

## Nested Cross Validation

### Inner Grid Search

    validator <- mlexperiments::MLNestedCV$new(
      learner = mllrnrs::LearnerLightgbm$new(
        metric_optimization_higher_better = FALSE
      ),
      strategy = "grid",
      fold_list = fold_list,
      k_tuning = 3L,
      ncores = ncores,
      seed = seed
    )

    validator$parameter_grid <- parameter_grid
    validator$learner_args <- learner_args
    validator$split_type <- "stratified"

    validator$predict_args <- predict_args
    validator$performance_metric <- performance_metric
    validator$performance_metric_args <- performance_metric_args
    validator$return_models <- return_models

    validator$set_data(
      x = train_x,
      y = train_y
    )

    validator_results <- validator$execute()
    #>
    #> CV fold: Fold1
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #>
    #> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [===============================================================================================] 10/10 (100%)
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> CV fold: Fold2
    #> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
    #> [LightGBM] [Info] Start training from score -1.423260
    #> [LightGBM] [Info] Start training from score -1.427452
    #> [LightGBM] [Info] Start training from score -0.655556
    #> [LightGBM] [Info] Start training from score -1.428460
    #> [LightGBM] [Info] Start training from score -1.420092
    #> [LightGBM] [Info] Start training from score -0.656564
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #> [LightGBM] [Info] Start training from score -1.423260
    #> [LightGBM] [Info] Start training from score -1.427452
    #> [LightGBM] [Info] Start training from score -0.655556
    #> [LightGBM] [Info] Start training from score -1.428460
    #> [LightGBM] [Info] Start training from score -1.420092
    #> [LightGBM] [Info] Start training from score -0.656564
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
    #> [LightGBM] [Info] Start training from score -1.423260
    #> [LightGBM] [Info] Start training from score -1.427452
    #> [LightGBM] [Info] Start training from score -0.655556
    #> [LightGBM] [Info] Start training from score -1.428460
    #> [LightGBM] [Info] Start training from score -1.420092
    #> [LightGBM] [Info] Start training from score -0.656564
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
    #> [LightGBM] [Info] Start training from score -1.423260
    #> [LightGBM] [Info] Start training from score -1.427452
    #> [LightGBM] [Info] Start training from score -0.655556
    #> [LightGBM] [Info] Start training from score -1.428460
    #> [LightGBM] [Info] Start training from score -1.420092
    #> [LightGBM] [Info] Start training from score -0.656564
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
    #> [LightGBM] [Info] Start training from score -1.423260
    #> [LightGBM] [Info] Start training from score -1.427452
    #> [LightGBM] [Info] Start training from score -0.655556
    #> [LightGBM] [Info] Start training from score -1.428460
    #> [LightGBM] [Info] Start training from score -1.420092
    #> [LightGBM] [Info] Start training from score -0.656564
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
    #> [LightGBM] [Info] Start training from score -1.423260
    #> [LightGBM] [Info] Start training from score -1.427452
    #> [LightGBM] [Info] Start training from score -0.655556
    #> [LightGBM] [Info] Start training from score -1.428460
    #> [LightGBM] [Info] Start training from score -1.420092
    #> [LightGBM] [Info] Start training from score -0.656564
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
    #> [LightGBM] [Info] Start training from score -1.423260
    #> [LightGBM] [Info] Start training from score -1.427452
    #> [LightGBM] [Info] Start training from score -0.655556
    #> [LightGBM] [Info] Start training from score -1.428460
    #> [LightGBM] [Info] Start training from score -1.420092
    #> [LightGBM] [Info] Start training from score -0.656564
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
    #> [LightGBM] [Info] Start training from score -1.423260
    #> [LightGBM] [Info] Start training from score -1.427452
    #> [LightGBM] [Info] Start training from score -0.655556
    #> [LightGBM] [Info] Start training from score -1.428460
    #> [LightGBM] [Info] Start training from score -1.420092
    #> [LightGBM] [Info] Start training from score -0.656564
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #> [LightGBM] [Info] Start training from score -1.423260
    #> [LightGBM] [Info] Start training from score -1.427452
    #> [LightGBM] [Info] Start training from score -0.655556
    #> [LightGBM] [Info] Start training from score -1.428460
    #> [LightGBM] [Info] Start training from score -1.420092
    #> [LightGBM] [Info] Start training from score -0.656564
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [===============================================================================================] 10/10 (100%)
    #> [LightGBM] [Info] Start training from score -1.423260
    #> [LightGBM] [Info] Start training from score -1.427452
    #> [LightGBM] [Info] Start training from score -0.655556
    #> [LightGBM] [Info] Start training from score -1.428460
    #> [LightGBM] [Info] Start training from score -1.420092
    #> [LightGBM] [Info] Start training from score -0.656564
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> CV fold: Fold3
    #> CV progress [========================================================================================================] 3/3 (100%)
    #>
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491
    #>
    #> Parameter settings [===============================================================================================] 10/10 (100%)
    #> [LightGBM] [Info] Start training from score -1.421241
    #> [LightGBM] [Info] Start training from score -1.429645
    #> [LightGBM] [Info] Start training from score -0.655482
    #> [LightGBM] [Info] Start training from score -1.424424
    #> [LightGBM] [Info] Start training from score -1.428634
    #> [LightGBM] [Info] Start training from score -0.654471
    #> [LightGBM] [Info] Start training from score -1.422251
    #> [LightGBM] [Info] Start training from score -1.426444
    #> [LightGBM] [Info] Start training from score -0.656491

    head(validator_results)
    #>     fold performance nrounds bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves max_depth verbose
    #> 1: Fold1   0.9663978      62              0.8              0.6                8           0.1         14        -1      -1
    #> 2: Fold2   0.9514170      64              0.8              1.0               10           0.2          6        -1      -1
    #> 3: Fold3   0.9610215     100              0.6              0.6                8           0.1          6        -1      -1
    #>     objective        metric num_class
    #> 1: multiclass multi_logloss         3
    #> 2: multiclass multi_logloss         3

### Inner Bayesian Optimization

    validator <- mlexperiments::MLNestedCV$new(
      learner = mllrnrs::LearnerLightgbm$new(
        metric_optimization_higher_better = FALSE
      ),
      strategy = "bayesian",
      fold_list = fold_list,
      k_tuning = 3L,
      ncores = ncores,
      seed = seed
    )

    validator$parameter_grid <- parameter_grid
    validator$learner_args <- learner_args
    validator$split_type <- "stratified"

    validator$parameter_bounds <- parameter_bounds
    validator$optim_args <- optim_args

    validator$predict_args <- predict_args
    validator$performance_metric <- performance_metric
    validator$performance_metric_args <- performance_metric_args
    validator$return_models <- TRUE

    validator$set_data(
      x = train_x,
      y = train_y
    )

    validator_results <- validator$execute()
    #>
    #> CV fold: Fold1
    #>
    #> Registering parallel backend using 4 cores.
    #>
    #> CV fold: Fold2
    #> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
    #>
    #> Registering parallel backend using 4 cores.
    #>
    #> CV fold: Fold3
    #> CV progress [========================================================================================================] 3/3 (100%)
    #>
    #> Registering parallel backend using 4 cores.

    head(validator_results)
    #>     fold performance bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves nrounds max_depth verbose
    #> 1: Fold1   0.9731183        0.2000000        0.2559232                2     0.1992638         20      52        -1      -1
    #> 2: Fold2   0.9527665        0.8293692        0.5664407                7     0.1203691         11      76        -1      -1
    #> 3: Fold3   0.9623656        0.4438889        0.3041453               10     0.1462295         11      67        -1      -1
    #>     objective        metric num_class
    #> 1: multiclass multi_logloss         3
    #> 2: multiclass multi_logloss         3
    #> 3: multiclass multi_logloss         3

## Holdout Test Dataset Performance

### Predict Outcome in Holdout Test Dataset

    preds_lightgbm <- mlexperiments::predictions(
      object = validator,
      newdata = test_x
    )

### Evaluate Performance on Holdout Test Dataset

    perf_lightgbm <- mlexperiments::performance(
      object = validator,
      prediction_results = preds_lightgbm,
      y_ground_truth = test_y
    )
    perf_lightgbm
    #>    model performance
    #> 1: Fold1   0.9613375
    #> 2: Fold2   0.9602926
    #> 3: Fold3   0.9582027

## Appendix I: Grid-Search with Target Weigths

Here, `lightgbm`’s `weight`-argument is used to rescale the case-weights during the training.

    # define the target weights
    y_weights <- ifelse(train_y == 1, 0.8, ifelse(train_y == 2, 1.2, 1))
    head(y_weights)
    #> [1] 1.2 1.2 0.0 0.8 0.8 0.0

    tuner_w_weights <- mlexperiments::MLTuneParameters$new(
      learner = mllrnrs::LearnerLightgbm$new(
        metric_optimization_higher_better = FALSE
      ),
      strategy = "grid",
      ncores = ncores,
      seed = seed
    )

    tuner_w_weights$parameter_grid <- parameter_grid
    tuner_w_weights$learner_args <- c(
      learner_args,
      list(case_weights = y_weights)
    )
    tuner_w_weights$split_type <- "stratified"

    tuner_w_weights$set_data(
      x = train_x,
      y = train_y
    )

    tuner_results_grid <- tuner_w_weights$execute(k = 3)
    #>
    #> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
    #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
    #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
    #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
    #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
    #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #> Parameter settings [===============================================================================================] 10/10 (100%)

    head(tuner_results_grid)
    #>    setting_id metric_optim_mean nrounds bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves max_depth
    #>         <int>             <num>   <int>            <num>            <num>            <num>         <num>      <num>     <int>
    #> 1:          1         0.1294454      29              0.6              0.6                4           0.2         18        -1
    #> 2:          2         0.1221349      51              0.8              1.0               10           0.2          6        -1
    #> 3:          3         0.2240799     100              0.8              0.8                4           0.1          2        -1
    #> 4:          4         0.1194221      75              1.0              0.8                4           0.1         10        -1
    #> 5:          5         0.1281037      32              1.0              0.6                6           0.2         18        -1
    #> 6:          6         0.1245721      60              1.0              1.0                8           0.1         14        -1
    #>    verbose  objective        metric num_class
    #>      <int>     <char>        <char>    <char>
    #> 1:      -1 multiclass multi_logloss         3
    #> 2:      -1 multiclass multi_logloss         3
    #> 3:      -1 multiclass multi_logloss         3
    #> 4:      -1 multiclass multi_logloss         3
    #> 5:      -1 multiclass multi_logloss         3
    #> 6:      -1 multiclass multi_logloss         3

## Appendix II: k-Fold Cross Validation with Target Weigths

    validator_w_weights <- mlexperiments::MLCrossValidation$new(
      learner = mllrnrs::LearnerLightgbm$new(
        metric_optimization_higher_better = FALSE
      ),
      fold_list = fold_list,
      ncores = ncores,
      seed = seed
    )

    # append the optimized setting from above with the newly created weights
    validator_w_weights$learner_args <- c(
      tuner_w_weights$results$best.setting[-1]
    )

    validator_w_weights$predict_args <- predict_args
    validator_w_weights$performance_metric <- performance_metric
    validator_w_weights$performance_metric_args <- performance_metric_args
    validator_w_weights$return_models <- return_models

    validator_w_weights$set_data(
      x = train_x,
      y = train_y
    )

    validator_results <- validator_w_weights$execute()
    #>
    #> CV fold: Fold1
    #>
    #> CV fold: Fold2
    #>
    #> CV fold: Fold3
    #> CV progress [========================================================================================================] 3/3 (100%)
    #>

    head(validator_results)
    #>      fold performance nrounds bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves max_depth verbose  objective
    #>    <char>       <num>   <int>            <num>            <num>            <num>         <num>      <num>     <int>   <int>     <char>
    #> 1:  Fold1   0.9704301      75                1              0.8                4           0.1         10        -1      -1 multiclass
    #> 2:  Fold2   0.9541161      75                1              0.8                4           0.1         10        -1      -1 multiclass
    #> 3:  Fold3   0.9610215      75                1              0.8                4           0.1         10        -1      -1 multiclass
    #>           metric num_class
    #>           <char>    <char>
    #> 1: multi_logloss         3
    #> 2: multi_logloss         3
    #> 3: multi_logloss         3