# nolint start
library(mlexperiments)
library(mllrnrs)The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
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# nolint start
library(mlexperiments)
library(mllrnrs)See https://github.com/kapsner/mllrnrs/blob/main/R/learner_xgboost.R for implementation details.
library(mlbench)
data("DNA")
dataset <- DNA |>
data.table::as.data.table() |>
na.omit()
feature_cols <- colnames(dataset)[160:180]
target_col <- "Class"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$train, .SD, .SDcols = feature_cols]
)
train_y <- as.integer(dataset[data_split$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)]) - 1Lfold_list <- splitTools::create_folds(
y = train_y,
k = 3,
type = "stratified",
seed = seed
)# required learner arguments, not optimized
learner_args <- list(
objective = "multi:softprob",
eval_metric = "mlogloss",
num_class = 3
)
# set arguments for predict function and performance metric,
# required for mlexperiments::MLCrossValidation and
# mlexperiments::MLNestedCV
predict_args <- list(reshape = TRUE)
performance_metric <- metric("bacc")
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$execute(k = 3)
#>
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Parameter settings [===============================================================================================] 10/10 (100%)
head(tuner_results_grid)
#> setting_id metric_optim_mean nrounds subsample colsample_bytree min_child_weight learning_rate max_depth objective
#> 1: 1 1.0107788 27 0.6 0.8 5 0.2 1 multi:softprob
#> 2: 2 0.9822161 35 1.0 0.8 5 0.1 5 multi:softprob
#> 3: 3 1.0097847 100 0.8 0.8 5 0.1 1 multi:softprob
#> 4: 4 0.9850296 20 0.6 0.8 5 0.2 5 multi:softprob
#> 5: 5 0.9807356 34 1.0 0.8 1 0.1 5 multi:softprob
#> 6: 6 0.9734746 46 0.8 0.8 5 0.1 5 multi:softprob
#> eval_metric num_class
#> 1: mlogloss 3
#> 2: mlogloss 3
#> 3: mlogloss 3
#> 4: mlogloss 3
#> 5: mlogloss 3
#> 6: mlogloss 3tuner <- mlexperiments::MLTuneParameters$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 1.934
#> 2: 0 2 1.0 0.8 5 0.1 5 NA FALSE TRUE 2.181
#> 3: 0 3 0.8 0.8 5 0.1 1 NA FALSE TRUE 2.060
#> 4: 0 4 0.6 0.8 5 0.2 5 NA FALSE TRUE 2.057
#> 5: 0 5 1.0 0.8 1 0.1 5 NA FALSE TRUE 1.422
#> 6: 0 6 0.8 0.8 5 0.1 5 NA FALSE TRUE 1.505
#> Score metric_optim_mean nrounds errorMessage objective eval_metric num_class
#> 1: -1.0093139 1.0093139 51 NA multi:softprob mlogloss 3
#> 2: -0.9842567 0.9842567 34 NA multi:softprob mlogloss 3
#> 3: -1.0097517 1.0097517 79 NA multi:softprob mlogloss 3
#> 4: -0.9766614 0.9766614 17 NA multi:softprob mlogloss 3
#> 5: -0.9851912 0.9851912 28 NA multi:softprob mlogloss 3
#> 6: -0.9755198 0.9755198 42 NA multi:softprob mlogloss 3validator <- 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$execute()
#>
#> CV fold: Fold1
#>
#> CV fold: Fold2
#>
#> CV fold: Fold3
#> CV progress [========================================================================================================] 3/3 (100%)
#>
head(validator_results)
#> fold performance subsample colsample_bytree min_child_weight learning_rate max_depth nrounds objective eval_metric
#> 1: Fold1 0.4685501 0.5356077 0.8312972 1 0.1099728 10 23 multi:softprob mlogloss
#> 2: Fold2 0.4179775 0.5356077 0.8312972 1 0.1099728 10 23 multi:softprob mlogloss
#> 3: Fold3 0.4718162 0.5356077 0.8312972 1 0.1099728 10 23 multi:softprob mlogloss
#> num_class
#> 1: 3
#> 2: 3
#> 3: 3validator <- mlexperiments::MLNestedCV$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 [============================>-------------------------------------------------------------------] 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: 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 [============================>-------------------------------------------------------------------] 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%)
head(validator_results)
#> fold performance nrounds subsample colsample_bytree min_child_weight learning_rate max_depth objective eval_metric
#> 1: Fold1 0.4304667 30 0.8 0.8 5 0.1 5 multi:softprob mlogloss
#> 2: Fold2 0.3907826 33 0.8 0.8 5 0.1 5 multi:softprob mlogloss
#> 3: Fold3 0.4183769 25 0.6 1.0 1 0.1 5 multi:softprob mlogloss
#> num_class
#> 1: 3
#> 2: 3
#> 3: 3validator <- 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$execute()
#>
#> CV fold: Fold1
#>
#> Registering parallel backend using 4 cores.
#>
#> CV fold: Fold2
#> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
#>
#> Registering parallel backend using 4 cores.
#>
#> CV fold: Fold3
#> CV progress [========================================================================================================] 3/3 (100%)
#>
#> Registering parallel backend using 4 cores.
head(validator_results)
#> fold performance subsample colsample_bytree min_child_weight learning_rate max_depth nrounds objective eval_metric
#> 1: Fold1 0.4607781 0.7285277 0.8178568 1 0.1099728 10 18 multi:softprob mlogloss
#> 2: Fold2 0.4306211 0.5578915 0.7352097 1 0.1099728 10 24 multi:softprob mlogloss
#> 3: Fold3 0.4464739 0.5001911 0.8708509 1 0.1099728 10 19 multi:softprob mlogloss
#> num_class
#> 1: 3
#> 2: 3
#> 3: 3preds_xgboost <- mlexperiments::predictions(
object = validator,
newdata = test_x
)perf_xgboost <- mlexperiments::performance(
object = validator,
prediction_results = preds_xgboost,
y_ground_truth = test_y
)
perf_xgboost
#> model performance
#> 1: Fold1 0.4628262
#> 2: Fold2 0.4503222
#> 3: Fold3 0.4535445Here, xgboost’s weight-argument is used to rescale the case-weights during the training.
# define the target weights
y_weights <- ifelse(train_y == 1, 0.8, ifelse(train_y == 2, 1.2, 1))
head(y_weights)
#> [1] 1.2 1.2 0.0 0.8 0.8 0.0tuner_w_weights <- mlexperiments::MLTuneParameters$new(
learner = mllrnrs::LearnerXgboost$new(
metric_optimization_higher_better = FALSE
),
strategy = "grid",
ncores = ncores,
seed = seed
)
tuner_w_weights$parameter_grid <- parameter_grid
tuner_w_weights$learner_args <- c(
learner_args,
list(case_weights = y_weights)
)
tuner_w_weights$split_type <- "stratified"
tuner_w_weights$set_data(
x = train_x,
y = train_y
)
tuner_results_grid <- tuner_w_weights$execute(k = 3)
#>
#> 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%)
head(tuner_results_grid)
#> setting_id metric_optim_mean nrounds subsample colsample_bytree min_child_weight learning_rate max_depth objective
#> <int> <num> <int> <num> <num> <num> <num> <num> <char>
#> 1: 1 0.9447465 27 0.6 0.8 5 0.2 1 multi:softprob
#> 2: 2 0.9222842 33 1.0 0.8 5 0.1 5 multi:softprob
#> 3: 3 0.9442046 100 0.8 0.8 5 0.1 1 multi:softprob
#> 4: 4 0.9236826 20 0.6 0.8 5 0.2 5 multi:softprob
#> 5: 5 0.9197338 35 1.0 0.8 1 0.1 5 multi:softprob
#> 6: 6 0.9147754 46 0.8 0.8 5 0.1 5 multi:softprob
#> eval_metric num_class
#> <char> <num>
#> 1: mlogloss 3
#> 2: mlogloss 3
#> 3: mlogloss 3
#> 4: mlogloss 3
#> 5: mlogloss 3
#> 6: mlogloss 3validator <- mlexperiments::MLCrossValidation$new(
learner = mllrnrs::LearnerXgboost$new(
metric_optimization_higher_better = FALSE
),
fold_list = fold_list,
ncores = ncores,
seed = seed
)
# append the optimized setting from above with the newly created weights
validator$learner_args <- c(
tuner$results$best.setting[-1],
list("case_weights" = y_weights)
)
validator$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
#> CV progress [========================================================================================================] 3/3 (100%)
#>
head(validator_results)
#> fold performance subsample colsample_bytree min_child_weight learning_rate max_depth nrounds objective eval_metric
#> <char> <num> <num> <num> <num> <num> <num> <int> <char> <char>
#> 1: Fold1 0.4508447 0.5356077 0.8312972 1 0.1099728 10 23 multi:softprob mlogloss
#> 2: Fold2 0.4185381 0.5356077 0.8312972 1 0.1099728 10 23 multi:softprob mlogloss
#> 3: Fold3 0.4436661 0.5356077 0.8312972 1 0.1099728 10 23 multi:softprob mlogloss
#> num_class
#> <num>
#> 1: 3
#> 2: 3
#> 3: 3These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.