# nolint start
library(mlexperiments)
library(mllrnrs)# nolint start
library(mlexperiments)
library(mllrnrs)See https://github.com/kapsner/mllrnrs/blob/main/R/learner_xgboost.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.xgb.nrounds" = 100L)
options("mlexperiments.optim.xgb.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$train, .SD, .SDcols = feature_cols]
)
train_y <- log(dataset[data_split$train, get(target_col)])
test_x <- model.matrix(
~ -1 + .,
dataset[data_split$test, .SD, .SDcols = feature_cols]
)
test_y <- log(dataset[data_split$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(
objective = "reg:squarederror",
eval_metric = "rmse"
)
# 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(
subsample = seq(0.6, 1, .2),
colsample_bytree = seq(0.6, 1, .2),
min_child_weight = seq(1, 5, 4),
learning_rate = seq(0.1, 0.2, 0.1),
max_depth = seq(1, 5, 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(
subsample = c(0.2, 1),
colsample_bytree = c(0.2, 1),
min_child_weight = c(1L, 10L),
learning_rate = c(0.1, 0.2),
max_depth = c(1L, 10L)
)
optim_args <- list(
iters.n = ncores,
kappa = 3.5,
acq = "ucb"
)tuner <- mlexperiments::MLTuneParameters$new(
learner = mllrnrs::LearnerXgboost$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$execute(k = 3)
#>
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Parameter settings [===============================================================================================] 10/10 (100%)
head(tuner_results_grid)
#> setting_id metric_optim_mean nrounds subsample colsample_bytree min_child_weight learning_rate max_depth objective
#> 1: 1 0.1865926 77 0.6 0.8 5 0.2 1 reg:squarederror
#> 2: 2 0.1612372 98 1.0 0.8 5 0.1 5 reg:squarederror
#> 3: 3 0.1933602 93 0.8 0.8 5 0.1 1 reg:squarederror
#> 4: 4 0.1615993 78 0.6 0.8 5 0.2 5 reg:squarederror
#> 5: 5 0.1648096 99 1.0 0.8 1 0.1 5 reg:squarederror
#> 6: 6 0.1573879 100 0.8 0.8 5 0.1 5 reg:squarederror
#> eval_metric
#> 1: rmse
#> 2: rmse
#> 3: rmse
#> 4: rmse
#> 5: rmse
#> 6: rmsetuner <- mlexperiments::MLTuneParameters$new(
learner = mllrnrs::LearnerXgboost$new(
metric_optimization_higher_better = FALSE
),
strategy = "bayesian",
ncores = ncores,
seed = seed
)
tuner$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 subsample colsample_bytree min_child_weight learning_rate max_depth gpUtility acqOptimum inBounds Elapsed
#> 1: 0 1 0.6 0.8 5 0.2 1 NA FALSE TRUE 1.569
#> 2: 0 2 1.0 0.8 5 0.1 5 NA FALSE TRUE 1.663
#> 3: 0 3 0.8 0.8 5 0.1 1 NA FALSE TRUE 1.611
#> 4: 0 4 0.6 0.8 5 0.2 5 NA FALSE TRUE 1.611
#> 5: 0 5 1.0 0.8 1 0.1 5 NA FALSE TRUE 0.941
#> 6: 0 6 0.8 0.8 5 0.1 5 NA FALSE TRUE 0.906
#> Score metric_optim_mean nrounds errorMessage objective eval_metric
#> 1: -0.1865024 0.1865024 56 NA reg:squarederror rmse
#> 2: -0.1607242 0.1607242 89 NA reg:squarederror rmse
#> 3: -0.1913163 0.1913163 100 NA reg:squarederror rmse
#> 4: -0.1609879 0.1609879 66 NA reg:squarederror rmse
#> 5: -0.1573682 0.1573682 100 NA reg:squarederror rmse
#> 6: -0.1635603 0.1635603 92 NA reg:squarederror rmsevalidator <- mlexperiments::MLCrossValidation$new(
learner = mllrnrs::LearnerXgboost$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$execute()
#>
#> CV fold: Fold1
#>
#> CV fold: Fold2
#>
#> CV fold: Fold3
head(validator_results)
#> fold performance subsample colsample_bytree min_child_weight learning_rate max_depth nrounds objective eval_metric
#> 1: Fold1 0.04193925 0.6 1 1 0.1 5 92 reg:squarederror rmse
#> 2: Fold2 0.05079392 0.6 1 1 0.1 5 92 reg:squarederror rmse
#> 3: Fold3 0.03915493 0.6 1 1 0.1 5 92 reg:squarederror rmsevalidator <- mlexperiments::MLNestedCV$new(
learner = mllrnrs::LearnerXgboost$new(
metric_optimization_higher_better = FALSE
),
strategy = "grid",
fold_list = fold_list,
k_tuning = 3L,
ncores = ncores,
seed = seed
)
validator$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
#>
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> CV fold: Fold2
#> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
#>
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> CV fold: Fold3
#> CV progress [========================================================================================================] 3/3 (100%)
#>
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Parameter settings [===============================================================================================] 10/10 (100%)
head(validator_results)
#> fold performance nrounds subsample colsample_bytree min_child_weight learning_rate max_depth objective eval_metric
#> 1: Fold1 0.04291802 64 0.8 0.8 5 0.1 5 reg:squarederror rmse
#> 2: Fold2 0.05138479 76 0.6 1.0 1 0.1 5 reg:squarederror rmse
#> 3: Fold3 0.03818053 36 0.6 0.8 5 0.2 5 reg:squarederror rmsevalidator <- mlexperiments::MLNestedCV$new(
learner = mllrnrs::LearnerXgboost$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 subsample colsample_bytree min_child_weight learning_rate max_depth nrounds objective eval_metric
#> 1: Fold1 0.04147964 0.6225939 0.9208933 5 0.1326066 5 59 reg:squarederror rmse
#> 2: Fold2 0.05881907 1.0000000 0.8000000 1 0.1000000 5 94 reg:squarederror rmse
#> 3: Fold3 0.03890190 0.6000000 1.0000000 5 0.2000000 5 37 reg:squarederror rmsepreds_xgboost <- mlexperiments::predictions(
object = validator,
newdata = test_x
)perf_xgboost <- mlexperiments::performance(
object = validator,
prediction_results = preds_xgboost,
y_ground_truth = test_y,
type = "regression"
)
perf_xgboost
#> model performance mse msle mae mape rmse rmsle rsq sse
#> 1: Fold1 0.04322328 0.02725729 0.001868252 0.1188479 0.04074989 0.1650978 0.04322328 0.8227146 4.224880
#> 2: Fold2 0.04730978 0.03081692 0.002238216 0.1235033 0.04247960 0.1755475 0.04730978 0.7995622 4.776623
#> 3: Fold3 0.03977942 0.02204549 0.001582402 0.1090010 0.03781531 0.1484773 0.03977942 0.8566129 3.417052