# nolint start
library(mlexperiments)
library(mllrnrs)# nolint start
library(mlexperiments)
library(mllrnrs)See https://github.com/kapsner/mllrnrs/blob/main/R/learner_lightgbm.R for implementation details.
library(mlbench)
data("BostonHousing")
dataset <- BostonHousing |>
data.table::as.data.table() |>
na.omit()
feature_cols <- colnames(dataset)[1:13]
target_col <- "medv"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$train, .SD, .SDcols = feature_cols]
)
train_y <- dataset[data_split$train, get(target_col)]
test_x <- model.matrix(
~ -1 + .,
dataset[data_split$test, .SD, .SDcols = feature_cols]
)
test_y <- dataset[data_split$test, get(target_col)]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 = "regression",
metric = "l2"
)
# set arguments for predict function and performance metric,
# required for mlexperiments::MLCrossValidation and
# mlexperiments::MLNestedCV
predict_args <- NULL
performance_metric <- metric("rmsle")
performance_metric_args <- NULL
return_models <- FALSE
# required for grid search and initialization of bayesian optimization
parameter_grid <- expand.grid(
bagging_fraction = seq(0.6, 1, .2),
feature_fraction = seq(0.6, 1, .2),
min_data_in_leaf = seq(2, 10, 2),
learning_rate = seq(0.1, 0.2, 0.1),
num_leaves = seq(2, 20, 4)
)
# reduce to a maximum of 10 rows
if (nrow(parameter_grid) > 10) {
set.seed(123)
sample_rows <- sample(seq_len(nrow(parameter_grid)), 10, FALSE)
parameter_grid <- kdry::mlh_subset(parameter_grid, sample_rows)
}
# required for bayesian optimization
parameter_bounds <- list(
bagging_fraction = c(0.2, 1),
feature_fraction = c(0.2, 1),
min_data_in_leaf = c(2L, 12L),
learning_rate = c(0.1, 0.2),
num_leaves = c(2L, 20L)
)
optim_args <- list(
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] Start training from score 22.450000
#> [LightGBM] [Info] Start training from score 22.655319
#> [LightGBM] [Info] Start training from score 22.592704
#> [LightGBM] [Info] Start training from score 22.450000
#> [LightGBM] [Info] Start training from score 22.655319
#> [LightGBM] [Info] Start training from score 22.592704
#> [LightGBM] [Info] Start training from score 22.450000
#> [LightGBM] [Info] Start training from score 22.655319
#> [LightGBM] [Info] Start training from score 22.592704
#> [LightGBM] [Info] Start training from score 22.450000
#> [LightGBM] [Info] Start training from score 22.655319
#> [LightGBM] [Info] Start training from score 22.592704
#>
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> [LightGBM] [Info] Start training from score 22.450000
#> [LightGBM] [Info] Start training from score 22.655319
#> [LightGBM] [Info] Start training from score 22.592704
#>
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> [LightGBM] [Info] Start training from score 22.450000
#> [LightGBM] [Info] Start training from score 22.655319
#> [LightGBM] [Info] Start training from score 22.592704
#>
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> [LightGBM] [Info] Start training from score 22.450000
#> [LightGBM] [Info] Start training from score 22.655319
#> [LightGBM] [Info] Start training from score 22.592704
#>
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> [LightGBM] [Info] Start training from score 22.450000
#> [LightGBM] [Info] Start training from score 22.655319
#> [LightGBM] [Info] Start training from score 22.592704
#>
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> [LightGBM] [Info] Start training from score 22.450000
#> [LightGBM] [Info] Start training from score 22.655319
#> [LightGBM] [Info] Start training from score 22.592704
#>
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> [LightGBM] [Info] Start training from score 22.450000
#> [LightGBM] [Info] Start training from score 22.655319
#> [LightGBM] [Info] Start training from score 22.592704
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 13.59085 85 0.6 0.6 4 0.2 18 -1
#> 2: 2 13.75483 55 0.8 1.0 10 0.2 6 -1
#> 3: 3 21.08526 58 0.8 0.8 4 0.1 2 -1
#> 4: 4 13.31343 92 1.0 0.8 4 0.1 10 -1
#> 5: 5 13.86649 80 1.0 0.6 6 0.2 18 -1
#> 6: 6 14.58646 100 1.0 1.0 8 0.1 14 -1
#> verbose objective metric
#> 1: -1 regression l2
#> 2: -1 regression l2
#> 3: -1 regression l2
#> 4: -1 regression l2
#> 5: -1 regression l2
#> 6: -1 regression l2tuner <- 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: 1.081 -13.59085 13.59085 85 NA -1 -1 regression l2
#> 2: 1.072 -13.75483 13.75483 55 NA -1 -1 regression l2
#> 3: 1.044 -21.08526 21.08526 58 NA -1 -1 regression l2
#> 4: 1.126 -13.31343 13.31343 92 NA -1 -1 regression l2
#> 5: 0.104 -13.86649 13.86649 80 NA -1 -1 regression l2
#> 6: 0.106 -14.58646 14.58646 100 NA -1 -1 regression l2validator <- 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$execute()
#>
#> CV fold: Fold1
#>
#> CV fold: Fold2
#>
#> CV fold: Fold3
head(validator_results)
#> fold performance bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves nrounds max_depth verbose
#> 1: Fold1 0.1572748 0.6 0.8 2 0.2 10 34 -1 -1
#> 2: Fold2 0.1770563 0.6 0.8 2 0.2 10 34 -1 -1
#> 3: Fold3 0.1439331 0.6 0.8 2 0.2 10 34 -1 -1
#> objective metric
#> 1: regression l2
#> 2: regression l2
#> 3: regression l2validator <- mlexperiments::MLNestedCV$new(
learner = mllrnrs::LearnerLightgbm$new(
metric_optimization_higher_better = FALSE
),
strategy = "grid",
fold_list = fold_list,
k_tuning = 3L,
ncores = ncores,
seed = seed
)
validator$parameter_grid <- parameter_grid
validator$learner_args <- learner_args
validator$split_type <- "stratified"
validator$predict_args <- predict_args
validator$performance_metric <- performance_metric
validator$performance_metric_args <- performance_metric_args
validator$return_models <- return_models
validator$set_data(
x = train_x,
y = train_y
)
validator_results <- validator$execute()
#>
#> CV fold: Fold1
#> [LightGBM] [Info] Start training from score 22.387821
#> [LightGBM] [Info] Start training from score 22.485257
#> [LightGBM] [Info] Start training from score 22.476923
#> [LightGBM] [Info] Start training from score 22.387821
#> [LightGBM] [Info] Start training from score 22.485257
#> [LightGBM] [Info] Start training from score 22.476923
#> [LightGBM] [Info] Start training from score 22.387821
#> [LightGBM] [Info] Start training from score 22.485257
#> [LightGBM] [Info] Start training from score 22.476923
#> [LightGBM] [Info] Start training from score 22.387821
#> [LightGBM] [Info] Start training from score 22.485257
#> [LightGBM] [Info] Start training from score 22.476923
#> [LightGBM] [Info] Start training from score 22.387821
#> [LightGBM] [Info] Start training from score 22.485257
#> [LightGBM] [Info] Start training from score 22.476923
#>
#>
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> [LightGBM] [Info] Start training from score 22.387821
#> [LightGBM] [Info] Start training from score 22.485257
#> [LightGBM] [Info] Start training from score 22.476923
#>
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> [LightGBM] [Info] Start training from score 22.387821
#> [LightGBM] [Info] Start training from score 22.485257
#> [LightGBM] [Info] Start training from score 22.476923
#>
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> [LightGBM] [Info] Start training from score 22.387821
#> [LightGBM] [Info] Start training from score 22.485257
#> [LightGBM] [Info] Start training from score 22.476923
#>
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> [LightGBM] [Info] Start training from score 22.387821
#> [LightGBM] [Info] Start training from score 22.485257
#> [LightGBM] [Info] Start training from score 22.476923
#>
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> [LightGBM] [Info] Start training from score 22.387821
#> [LightGBM] [Info] Start training from score 22.485257
#> [LightGBM] [Info] Start training from score 22.476923
#>
#> CV fold: Fold2
#> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
#> [LightGBM] [Info] Start training from score 22.517722
#> [LightGBM] [Info] Start training from score 22.641401
#> [LightGBM] [Info] Start training from score 22.809677
#> [LightGBM] [Info] Start training from score 22.517722
#> [LightGBM] [Info] Start training from score 22.641401
#> [LightGBM] [Info] Start training from score 22.809677
#> [LightGBM] [Info] Start training from score 22.517722
#> [LightGBM] [Info] Start training from score 22.641401
#> [LightGBM] [Info] Start training from score 22.809677
#>
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> [LightGBM] [Info] Start training from score 22.517722
#> [LightGBM] [Info] Start training from score 22.641401
#> [LightGBM] [Info] Start training from score 22.809677
#>
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> [LightGBM] [Info] Start training from score 22.517722
#> [LightGBM] [Info] Start training from score 22.641401
#> [LightGBM] [Info] Start training from score 22.809677
#>
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> [LightGBM] [Info] Start training from score 22.517722
#> [LightGBM] [Info] Start training from score 22.641401
#> [LightGBM] [Info] Start training from score 22.809677
#>
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> [LightGBM] [Info] Start training from score 22.517722
#> [LightGBM] [Info] Start training from score 22.641401
#> [LightGBM] [Info] Start training from score 22.809677
#>
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> [LightGBM] [Info] Start training from score 22.517722
#> [LightGBM] [Info] Start training from score 22.641401
#> [LightGBM] [Info] Start training from score 22.809677
#>
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> [LightGBM] [Info] Start training from score 22.517722
#> [LightGBM] [Info] Start training from score 22.641401
#> [LightGBM] [Info] Start training from score 22.809677
#>
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> [LightGBM] [Info] Start training from score 22.517722
#> [LightGBM] [Info] Start training from score 22.641401
#> [LightGBM] [Info] Start training from score 22.809677
#>
#> CV fold: Fold3
#> CV progress [========================================================================================================] 3/3 (100%)
#>
#> [LightGBM] [Info] Start training from score 22.496129
#> [LightGBM] [Info] Start training from score 22.728387
#> [LightGBM] [Info] Start training from score 22.553846
#> [LightGBM] [Info] Start training from score 22.496129
#> [LightGBM] [Info] Start training from score 22.728387
#> [LightGBM] [Info] Start training from score 22.553846
#> [LightGBM] [Info] Start training from score 22.496129
#> [LightGBM] [Info] Start training from score 22.728387
#> [LightGBM] [Info] Start training from score 22.553846
#> [LightGBM] [Info] Start training from score 22.496129
#> [LightGBM] [Info] Start training from score 22.728387
#> [LightGBM] [Info] Start training from score 22.553846
#> [LightGBM] [Info] Start training from score 22.496129
#> [LightGBM] [Info] Start training from score 22.728387
#> [LightGBM] [Info] Start training from score 22.553846
#> [LightGBM] [Info] Start training from score 22.496129
#> [LightGBM] [Info] Start training from score 22.728387
#> [LightGBM] [Info] Start training from score 22.553846
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#>
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> [LightGBM] [Info] Start training from score 22.496129
#> [LightGBM] [Info] Start training from score 22.728387
#> [LightGBM] [Info] Start training from score 22.553846
#>
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> [LightGBM] [Info] Start training from score 22.496129
#> [LightGBM] [Info] Start training from score 22.728387
#> [LightGBM] [Info] Start training from score 22.553846
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#>
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> [LightGBM] [Info] Start training from score 22.496129
#> [LightGBM] [Info] Start training from score 22.728387
#> [LightGBM] [Info] Start training from score 22.553846
#>
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> [LightGBM] [Info] Start training from score 22.496129
#> [LightGBM] [Info] Start training from score 22.728387
#> [LightGBM] [Info] Start training from score 22.553846
head(validator_results)
#> fold performance nrounds bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves max_depth verbose
#> 1: Fold1 0.1856914 99 0.8 0.8 4 0.1 2 -1 -1
#> 2: Fold2 0.1842789 37 0.8 0.6 8 0.1 14 -1 -1
#> 3: Fold3 0.1516625 17 0.6 0.6 4 0.2 18 -1 -1
#> objective metric
#> 1: regression l2
#> 2: regression l2
#> 3: regression l2validator <- 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 0.1972673 1.0000000 0.2000000 7 0.1000000 2 100 -1 -1
#> 2: Fold2 0.1934754 0.5029800 0.4977050 7 0.1195995 4 52 -1 -1
#> 3: Fold3 0.1391255 0.8050493 0.5902201 2 0.1458152 20 44 -1 -1
#> objective metric
#> 1: regression l2
#> 2: regression l2
#> 3: regression l2preds_lightgbm <- mlexperiments::predictions(
object = validator,
newdata = test_x
)perf_lightgbm <- mlexperiments::performance(
object = validator,
prediction_results = preds_lightgbm,
y_ground_truth = test_y,
type = "regression"
)
perf_lightgbm
#> model performance mse msle mae mape rmse rmsle rsq sse
#> 1: Fold1 0.1593611 16.258140 0.02539596 2.700096 0.1230206 4.032138 0.1593611 0.7945966 2520.012
#> 2: Fold2 0.1720557 12.614833 0.02960318 2.629003 0.1329465 3.551737 0.1720557 0.8406257 1955.299
#> 3: Fold3 0.1424064 9.666178 0.02027958 2.226562 0.1101779 3.109048 0.1424064 0.8778787 1498.258