Nothing
inst/doc/mllrnrs_lightgbm_binary.R
In mllrnrs: R6-Based ML Learners for 'mlexperiments'
## ----setup--------------------------------------------------------------------
# nolint start
library(mlexperiments)
library(mllrnrs)
## -----------------------------------------------------------------------------
library(mlbench)
data("PimaIndiansDiabetes2")
dataset <- PimaIndiansDiabetes2 |>
data.table::as.data.table() |>
na.omit()
feature_cols <- colnames(dataset)[1:8]
target_col <- "diabetes"
## -----------------------------------------------------------------------------
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)
## -----------------------------------------------------------------------------
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
## -----------------------------------------------------------------------------
fold_list <- splitTools::create_folds(
y = train_y,
k = 3,
type = "stratified",
seed = seed
)
## -----------------------------------------------------------------------------
# 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"
)
## -----------------------------------------------------------------------------
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
## -----------------------------------------------------------------------------
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
## -----------------------------------------------------------------------------
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$execute()
#>
#> CV fold: Fold1
#>
#> CV fold: Fold2
#>
#> CV fold: Fold3
head(validator_results)
#> fold performance bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves nrounds max_depth verbose
#> 1: Fold1 0.8683236 0.4344866 1 2 0.1 5 38 -1 -1
#> 2: Fold2 0.8841883 0.4344866 1 2 0.1 5 38 -1 -1
#> 3: Fold3 0.8846806 0.4344866 1 2 0.1 5 38 -1 -1
#> objective metric
#> 1: binary binary_logloss
#> 2: binary binary_logloss
#> 3: binary binary_logloss
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLNestedCV$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
## -----------------------------------------------------------------------------
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
## -----------------------------------------------------------------------------
preds_lightgbm <- mlexperiments::predictions(
object = validator,
newdata = test_x
)
## -----------------------------------------------------------------------------
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
## ----include=FALSE------------------------------------------------------------
# nolint end
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## ----setup--------------------------------------------------------------------
# nolint start
library(mlexperiments)
library(mllrnrs)
## -----------------------------------------------------------------------------
library(mlbench)
data("PimaIndiansDiabetes2")
dataset <- PimaIndiansDiabetes2 |>
data.table::as.data.table() |>
na.omit()
feature_cols <- colnames(dataset)[1:8]
target_col <- "diabetes"
## -----------------------------------------------------------------------------
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)
## -----------------------------------------------------------------------------
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$test, get(target_col)]) - 1L
## -----------------------------------------------------------------------------
fold_list <- splitTools::create_folds(
y = train_y,
k = 3,
type = "stratified",
seed = seed
)
## -----------------------------------------------------------------------------
# required learner arguments, not optimized
learner_args <- list(
max_depth = -1L,
verbose = -1L,
objective = "binary",
metric = "binary_logloss"
)
# set arguments for predict function and performance metric,
# required for mlexperiments::MLCrossValidation and
# mlexperiments::MLNestedCV
predict_args <- NULL
performance_metric <- metric("auc")
performance_metric_args <- list(positive = "1")
return_models <- FALSE
# required for grid search and initialization of bayesian optimization
parameter_grid <- expand.grid(
bagging_fraction = seq(0.6, 1, .2),
feature_fraction = seq(0.6, 1, .2),
min_data_in_leaf = seq(2, 10, 2),
learning_rate = seq(0.1, 0.2, 0.1),
num_leaves = seq(2, 20, 4)
)
# reduce to a maximum of 10 rows
if (nrow(parameter_grid) > 10) {
set.seed(123)
sample_rows <- sample(seq_len(nrow(parameter_grid)), 10, FALSE)
parameter_grid <- kdry::mlh_subset(parameter_grid, sample_rows)
}
# required for bayesian optimization
parameter_bounds <- list(
bagging_fraction = c(0.2, 1),
feature_fraction = c(0.2, 1),
min_data_in_leaf = c(2L, 12L),
learning_rate = c(0.1, 0.2),
num_leaves = c(2L, 20L)
)
optim_args <- list(
iters.n = ncores,
kappa = 3.5,
acq = "ucb"
)
## -----------------------------------------------------------------------------
tuner <- mlexperiments::MLTuneParameters$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
## -----------------------------------------------------------------------------
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$execute(k = 3)
#>
#> Registering parallel backend using 4 cores.
head(tuner_results_bayesian)
#> Epoch setting_id bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves gpUtility acqOptimum inBounds
#> 1: 0 1 0.6 0.6 4 0.2 18 NA FALSE TRUE
#> 2: 0 2 0.8 1.0 10 0.2 6 NA FALSE TRUE
#> 3: 0 3 0.8 0.8 4 0.1 2 NA FALSE TRUE
#> 4: 0 4 1.0 0.8 4 0.1 10 NA FALSE TRUE
#> 5: 0 5 1.0 0.6 6 0.2 18 NA FALSE TRUE
#> 6: 0 6 1.0 1.0 8 0.1 14 NA FALSE TRUE
#> Elapsed Score metric_optim_mean nrounds errorMessage max_depth verbose objective metric
#> 1: 0.972 -0.4270896 0.4270896 15 NA -1 -1 binary binary_logloss
#> 2: 0.951 -0.3978536 0.3978536 14 NA -1 -1 binary binary_logloss
#> 3: 0.974 -0.4011304 0.4011304 95 NA -1 -1 binary binary_logloss
#> 4: 0.971 -0.4021737 0.4021737 30 NA -1 -1 binary binary_logloss
#> 5: 0.039 -0.4034704 0.4034704 14 NA -1 -1 binary binary_logloss
#> 6: 0.045 -0.3955430 0.3955430 28 NA -1 -1 binary binary_logloss
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLCrossValidation$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
## -----------------------------------------------------------------------------
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
## -----------------------------------------------------------------------------
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
## -----------------------------------------------------------------------------
preds_lightgbm <- mlexperiments::predictions(
object = validator,
newdata = test_x
)
## -----------------------------------------------------------------------------
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
## ----include=FALSE------------------------------------------------------------
# nolint end
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