mllrnrs_lightgbm_binary

• Preprocessing
  • Import and Prepare Data
  • General Configurations
  • Generate Training- and Test Data
  • Generate Training Data Folds
• Experiments
  • Prepare Experiments
  • Hyperparameter Tuning
    • Grid Search
    • Bayesian Optimization
  • k-Fold Cross Validation
  • Nested Cross Validation
    • Inner Grid Search
    • Inner Bayesian Optimization
  • Holdout Test Dataset Performance
    • Predict Outcome in Holdout Test Dataset
    • Evaluate Performance on Holdout Test Dataset

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" = 10L)
options("mlexperiments.optim.lgb.nrounds" = 100L)
options("mlexperiments.optim.lgb.early_stopping_rounds" = 10L)

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")
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(
  iters.n = 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     prauc sensitivity specificity       ppv       npv tn tp fn fp       tnr       tpr       fnr
#> 1: Fold1   0.8075300 0.8075300 0.6470427   0.4871795   0.8607595 0.6333333 0.7727273 68 19 20 11 0.8607595 0.4871795 0.5128205
#> 2: Fold2   0.7695553 0.7695553 0.5825168   0.3846154   0.8987342 0.6521739 0.7473684 71 15 24  8 0.8987342 0.3846154 0.6153846
#> 3: Fold3   0.7914638 0.7914638 0.6164725   0.4615385   0.8734177 0.6428571 0.7666667 69 18 21 10 0.8734177 0.4615385 0.5384615
#>          fpr    bbrier       acc        ce     fbeta
#> 1: 0.1392405 0.1632361 0.7372881 0.2627119 0.5507246
#> 2: 0.1012658 0.1851544 0.7288136 0.2711864 0.4838710
#> 3: 0.1265823 0.1741526 0.7372881 0.2627119 0.5373134