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

    feature_cols <- colnames(dataset)[1:8]
    target_col <- "diabetes"

## 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 = "binary",
      metric = "binary_logloss"
    )

    # set arguments for predict function and performance metric,
    # required for mlexperiments::MLCrossValidation and
    # mlexperiments::MLNestedCV
    predict_args <- NULL
    performance_metric <- metric("auc")
    performance_metric_args <- list(positive = "1", negative = "0")
    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] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336957 -> initscore=-0.676887
    #> [LightGBM] [Info] Start training from score -0.676887
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336957 -> initscore=-0.676887
    #> [LightGBM] [Info] Start training from score -0.676887
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336957 -> initscore=-0.676887
    #> [LightGBM] [Info] Start training from score -0.676887
    #>
    #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336957 -> initscore=-0.676887
    #> [LightGBM] [Info] Start training from score -0.676887
    #>
    #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336957 -> initscore=-0.676887
    #> [LightGBM] [Info] Start training from score -0.676887
    #>
    #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336957 -> initscore=-0.676887
    #> [LightGBM] [Info] Start training from score -0.676887
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #>
    #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336957 -> initscore=-0.676887
    #> [LightGBM] [Info] Start training from score -0.676887
    #>
    #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336957 -> initscore=-0.676887
    #> [LightGBM] [Info] Start training from score -0.676887
    #>
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336957 -> initscore=-0.676887
    #> [LightGBM] [Info] Start training from score -0.676887
    #>
    #> Parameter settings [===============================================================================================] 10/10 (100%)
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.329670 -> initscore=-0.709676
    #> [LightGBM] [Info] Start training from score -0.709676
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336957 -> initscore=-0.676887
    #> [LightGBM] [Info] Start training from score -0.676887

    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         0.4270896      15              0.6              0.6                4           0.2         18        -1
    #> 2:          2         0.3978536      14              0.8              1.0               10           0.2          6        -1
    #> 3:          3         0.4011304      95              0.8              0.8                4           0.1          2        -1
    #> 4:          4         0.4021737      30              1.0              0.8                4           0.1         10        -1
    #> 5:          5         0.4034704      14              1.0              0.6                6           0.2         18        -1
    #> 6:          6         0.3955430      28              1.0              1.0                8           0.1         14        -1
    #>    verbose objective         metric
    #> 1:      -1    binary binary_logloss
    #> 2:      -1    binary binary_logloss
    #> 3:      -1    binary binary_logloss
    #> 4:      -1    binary binary_logloss
    #> 5:      -1    binary binary_logloss
    #> 6:      -1    binary binary_logloss

### 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
    #> 1:   0.972 -0.4270896         0.4270896      15           NA        -1      -1    binary binary_logloss
    #> 2:   0.951 -0.3978536         0.3978536      14           NA        -1      -1    binary binary_logloss
    #> 3:   0.974 -0.4011304         0.4011304      95           NA        -1      -1    binary binary_logloss
    #> 4:   0.971 -0.4021737         0.4021737      30           NA        -1      -1    binary binary_logloss
    #> 5:   0.039 -0.4034704         0.4034704      14           NA        -1      -1    binary binary_logloss
    #> 6:   0.045 -0.3955430         0.3955430      28           NA        -1      -1    binary binary_logloss

## 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 fold: Fold3

    head(validator_results)
    #>     fold performance bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves nrounds max_depth verbose
    #> 1: Fold1   0.8683236        0.4344866                1                2           0.1          5      38        -1      -1
    #> 2: Fold2   0.8841883        0.4344866                1                2           0.1          5      38        -1      -1
    #> 3: Fold3   0.8846806        0.4344866                1                2           0.1          5      38        -1      -1
    #>    objective         metric
    #> 1:    binary binary_logloss
    #> 2:    binary binary_logloss
    #> 3:    binary binary_logloss

## 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] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #>
    #>
    #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #>
    #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #>
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #>
    #> Parameter settings [===============================================================================================] 10/10 (100%)
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #>
    #> CV fold: Fold2
    #> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #>
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #>
    #> Parameter settings [===============================================================================================] 10/10 (100%)
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.327869 -> initscore=-0.717840
    #> [LightGBM] [Info] Start training from score -0.717840
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.330579 -> initscore=-0.705570
    #> [LightGBM] [Info] Start training from score -0.705570
    #>
    #> CV fold: Fold3
    #> CV progress [========================================================================================================] 3/3 (100%)
    #>
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.333333 -> initscore=-0.693147
    #> [LightGBM] [Info] Start training from score -0.693147
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.341463 -> initscore=-0.656780
    #> [LightGBM] [Info] Start training from score -0.656780
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336066 -> initscore=-0.680877
    #> [LightGBM] [Info] Start training from score -0.680877
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.333333 -> initscore=-0.693147
    #> [LightGBM] [Info] Start training from score -0.693147
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.341463 -> initscore=-0.656780
    #> [LightGBM] [Info] Start training from score -0.656780
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336066 -> initscore=-0.680877
    #> [LightGBM] [Info] Start training from score -0.680877
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.333333 -> initscore=-0.693147
    #> [LightGBM] [Info] Start training from score -0.693147
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.341463 -> initscore=-0.656780
    #> [LightGBM] [Info] Start training from score -0.656780
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336066 -> initscore=-0.680877
    #> [LightGBM] [Info] Start training from score -0.680877
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.333333 -> initscore=-0.693147
    #> [LightGBM] [Info] Start training from score -0.693147
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.341463 -> initscore=-0.656780
    #> [LightGBM] [Info] Start training from score -0.656780
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336066 -> initscore=-0.680877
    #> [LightGBM] [Info] Start training from score -0.680877
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.333333 -> initscore=-0.693147
    #> [LightGBM] [Info] Start training from score -0.693147
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.341463 -> initscore=-0.656780
    #> [LightGBM] [Info] Start training from score -0.656780
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336066 -> initscore=-0.680877
    #> [LightGBM] [Info] Start training from score -0.680877
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.333333 -> initscore=-0.693147
    #> [LightGBM] [Info] Start training from score -0.693147
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.341463 -> initscore=-0.656780
    #> [LightGBM] [Info] Start training from score -0.656780
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336066 -> initscore=-0.680877
    #> [LightGBM] [Info] Start training from score -0.680877
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.333333 -> initscore=-0.693147
    #> [LightGBM] [Info] Start training from score -0.693147
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.341463 -> initscore=-0.656780
    #> [LightGBM] [Info] Start training from score -0.656780
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336066 -> initscore=-0.680877
    #> [LightGBM] [Info] Start training from score -0.680877
    #>
    #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.333333 -> initscore=-0.693147
    #> [LightGBM] [Info] Start training from score -0.693147
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.341463 -> initscore=-0.656780
    #> [LightGBM] [Info] Start training from score -0.656780
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336066 -> initscore=-0.680877
    #> [LightGBM] [Info] Start training from score -0.680877
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #>
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.333333 -> initscore=-0.693147
    #> [LightGBM] [Info] Start training from score -0.693147
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.341463 -> initscore=-0.656780
    #> [LightGBM] [Info] Start training from score -0.656780
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336066 -> initscore=-0.680877
    #> [LightGBM] [Info] Start training from score -0.680877
    #>
    #> Parameter settings [===============================================================================================] 10/10 (100%)
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.333333 -> initscore=-0.693147
    #> [LightGBM] [Info] Start training from score -0.693147
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.341463 -> initscore=-0.656780
    #> [LightGBM] [Info] Start training from score -0.656780
    #> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.336066 -> initscore=-0.680877
    #> [LightGBM] [Info] Start training from score -0.680877

    head(validator_results)
    #>     fold performance nrounds bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves max_depth verbose
    #> 1: Fold1   0.8572184      72              0.8              0.8                4           0.1          2        -1      -1
    #> 2: Fold2   0.8625066      22              0.8              0.6                8           0.1         14        -1      -1
    #> 3: Fold3   0.8725269      53              0.8              0.8                4           0.1          2        -1      -1
    #>    objective         metric
    #> 1:    binary binary_logloss
    #> 2:    binary binary_logloss
    #> 3:    binary binary_logloss

### 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   0.8572184              0.8        0.8000000                4           0.1          2      72        -1      -1
    #> 2: Fold2   0.8730830              1.0        0.6198464               10           0.1         20      23        -1      -1
    #> 3: Fold3   0.8725269              0.8        0.8000000                4           0.1          2      53        -1      -1
    #>    objective         metric
    #> 1:    binary binary_logloss
    #> 2:    binary binary_logloss
    #> 3:    binary binary_logloss

## 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,
      type = "binary"
    )
    perf_lightgbm
    #>     model performance       AUC     Brier BrierScaled       BAC    TP    TN    FP    FN       TPR       TNR       FPR       FNR       PPV
    #>    <char>       <num>     <num>     <num>       <num>     <num> <int> <int> <int> <int>     <num>     <num>     <num>     <num>     <num>
    #> 1:  Fold1   0.8075300 0.8075300 0.1632361   0.2622850 0.6739695    19    68    11    20 0.4871795 0.8607595 0.1392405 0.5128205 0.6333333
    #> 2:  Fold2   0.7695553 0.7695553 0.1851544   0.1632297 0.6416748    15    71     8    24 0.3846154 0.8987342 0.1012658 0.6153846 0.6521739
    #> 3:  Fold3   0.7914638 0.7914638 0.1741526   0.2129501 0.6674781    18    69    10    21 0.4615385 0.8734177 0.1265823 0.5384615 0.6428571
    #>          NPV       FDR       MCC        F1     GMEAN       GPR       ACC      MMCE       BER
    #>        <num>     <num>     <num>     <num>     <num>     <num>     <num>     <num>     <num>
    #> 1: 0.7727273 0.3666667 0.3758781 0.5507246 0.6475680 0.5554701 0.7372881 0.2627119 0.3260305
    #> 2: 0.7473684 0.3478261 0.3364672 0.4838710 0.5879345 0.5008354 0.7288136 0.2711864 0.3583252
    #> 3: 0.7666667 0.3571429 0.3703681 0.5373134 0.6349141 0.5447048 0.7372881 0.2627119 0.3325219