Nothing
inst/doc/mllrnrs_xgboost_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.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)]) - 1L
## -----------------------------------------------------------------------------
fold_list <- splitTools::create_folds(
y = train_y,
k = 3,
type = "stratified",
seed = seed
)
## -----------------------------------------------------------------------------
# required learner arguments, not optimized
learner_args <- list(
objective = "binary:logistic",
eval_metric = "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(
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$execute(k = 3)
#>
#> Parameter settings [==================>-----------------------------------------------------------------------------] 2/10 ( 20%)
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Parameter settings [===============================================================================================] 10/10 (100%)
head(tuner_results_grid)
#> setting_id metric_optim_mean nrounds subsample colsample_bytree min_child_weight learning_rate max_depth objective
#> 1: 1 0.4121967 34 0.6 0.8 5 0.2 1 binary:logistic
#> 2: 2 0.3890956 57 1.0 0.8 5 0.1 5 binary:logistic
#> 3: 3 0.3925308 100 0.8 0.8 5 0.1 1 binary:logistic
#> 4: 4 0.4082505 34 0.6 0.8 5 0.2 5 binary:logistic
#> 5: 5 0.3975907 36 1.0 0.8 1 0.1 5 binary:logistic
#> 6: 6 0.3932451 66 0.8 0.8 5 0.1 5 binary:logistic
#> eval_metric
#> 1: logloss
#> 2: logloss
#> 3: logloss
#> 4: logloss
#> 5: logloss
#> 6: logloss
## -----------------------------------------------------------------------------
tuner <- mlexperiments::MLTuneParameters$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 1.695
#> 2: 0 2 1.0 0.8 5 0.1 5 NA FALSE TRUE 1.702
#> 3: 0 3 0.8 0.8 5 0.1 1 NA FALSE TRUE 1.734
#> 4: 0 4 0.6 0.8 5 0.2 5 NA FALSE TRUE 1.724
#> 5: 0 5 1.0 0.8 1 0.1 5 NA FALSE TRUE 0.849
#> 6: 0 6 0.8 0.8 5 0.1 5 NA FALSE TRUE 0.850
#> Score metric_optim_mean nrounds errorMessage objective eval_metric
#> 1: -0.4089735 0.4089735 56 NA binary:logistic logloss
#> 2: -0.3970937 0.3970937 49 NA binary:logistic logloss
#> 3: -0.4013240 0.4013240 100 NA binary:logistic logloss
#> 4: -0.4070968 0.4070968 69 NA binary:logistic logloss
#> 5: -0.3819756 0.3819756 39 NA binary:logistic logloss
#> 6: -0.3987643 0.3987643 99 NA binary:logistic logloss
## -----------------------------------------------------------------------------
validator <- 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$execute()
#>
#> CV fold: Fold1
#>
#> CV fold: Fold2
#>
#> CV fold: Fold3
head(validator_results)
#> fold performance subsample colsample_bytree min_child_weight learning_rate max_depth nrounds objective eval_metric
#> 1: Fold1 0.8799577 1 0.8 1 0.1 5 39 binary:logistic logloss
#> 2: Fold2 0.8635643 1 0.8 1 0.1 5 39 binary:logistic logloss
#> 3: Fold3 0.9027699 1 0.8 1 0.1 5 39 binary:logistic logloss
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLNestedCV$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 [==================================================================>-----------------------------] 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 [==================================================================>-----------------------------] 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 0.8675304 40 0.6 1 1 0.2 1 binary:logistic logloss
#> 2: Fold2 0.8635643 44 1.0 1 5 0.1 5 binary:logistic logloss
#> 3: Fold3 0.8793103 24 0.6 1 1 0.2 1 binary:logistic logloss
## -----------------------------------------------------------------------------
validator <- 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 0.8662084 0.6 1.0 1 0.2 1 28 binary:logistic logloss
#> 2: Fold2 0.8746695 1.0 0.8 5 0.1 5 44 binary:logistic logloss
#> 3: Fold3 0.8903335 0.6 1.0 1 0.1 5 30 binary:logistic logloss
## -----------------------------------------------------------------------------
preds_xgboost <- mlexperiments::predictions(
object = validator,
newdata = test_x
)
## -----------------------------------------------------------------------------
perf_xgboost <- mlexperiments::performance(
object = validator,
prediction_results = preds_xgboost,
y_ground_truth = test_y,
type = "binary"
)
perf_xgboost
#> model performance auc prauc sensitivity specificity ppv npv tn tp fn fp tnr tpr fnr
#> 1: Fold1 0.7922752 0.7922752 0.6016630 0.5128205 0.8734177 0.6666667 0.7840909 69 20 19 10 0.8734177 0.5128205 0.4871795
#> 2: Fold2 0.7687439 0.7687439 0.5601442 0.3846154 0.8860759 0.6250000 0.7446809 70 15 24 9 0.8860759 0.3846154 0.6153846
#> 3: Fold3 0.7594937 0.7594937 0.6142299 0.4871795 0.8481013 0.6129032 0.7701149 67 19 20 12 0.8481013 0.4871795 0.5128205
#> fpr bbrier acc ce fbeta
#> 1: 0.1265823 0.1726355 0.7542373 0.2457627 0.5797101
#> 2: 0.1139241 0.1885316 0.7203390 0.2796610 0.4761905
#> 3: 0.1518987 0.1854326 0.7288136 0.2711864 0.5428571
## ----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.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$test, get(target_col)]) - 1L
## -----------------------------------------------------------------------------
fold_list <- splitTools::create_folds(
y = train_y,
k = 3,
type = "stratified",
seed = seed
)
## -----------------------------------------------------------------------------
# required learner arguments, not optimized
learner_args <- list(
objective = "binary:logistic",
eval_metric = "logloss"
)
# set arguments for predict function and performance metric,
# required for mlexperiments::MLCrossValidation and
# mlexperiments::MLNestedCV
predict_args <- NULL
performance_metric <- metric("auc")
performance_metric_args <- list(positive = "1")
return_models <- FALSE
# required for grid search and initialization of bayesian optimization
parameter_grid <- expand.grid(
subsample = seq(0.6, 1, .2),
colsample_bytree = seq(0.6, 1, .2),
min_child_weight = seq(1, 5, 4),
learning_rate = seq(0.1, 0.2, 0.1),
max_depth = seq(1, 5, 4)
)
# reduce to a maximum of 10 rows
if (nrow(parameter_grid) > 10) {
set.seed(123)
sample_rows <- sample(seq_len(nrow(parameter_grid)), 10, FALSE)
parameter_grid <- kdry::mlh_subset(parameter_grid, sample_rows)
}
# required for bayesian optimization
parameter_bounds <- list(
subsample = c(0.2, 1),
colsample_bytree = c(0.2, 1),
min_child_weight = c(1L, 10L),
learning_rate = c(0.1, 0.2),
max_depth = c(1L, 10L)
)
optim_args <- list(
iters.n = ncores,
kappa = 3.5,
acq = "ucb"
)
## -----------------------------------------------------------------------------
tuner <- mlexperiments::MLTuneParameters$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 [==================>-----------------------------------------------------------------------------] 2/10 ( 20%)
#> 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 0.4121967 34 0.6 0.8 5 0.2 1 binary:logistic
#> 2: 2 0.3890956 57 1.0 0.8 5 0.1 5 binary:logistic
#> 3: 3 0.3925308 100 0.8 0.8 5 0.1 1 binary:logistic
#> 4: 4 0.4082505 34 0.6 0.8 5 0.2 5 binary:logistic
#> 5: 5 0.3975907 36 1.0 0.8 1 0.1 5 binary:logistic
#> 6: 6 0.3932451 66 0.8 0.8 5 0.1 5 binary:logistic
#> eval_metric
#> 1: logloss
#> 2: logloss
#> 3: logloss
#> 4: logloss
#> 5: logloss
#> 6: logloss
## -----------------------------------------------------------------------------
tuner <- 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$execute(k = 3)
#>
#> Registering parallel backend using 4 cores.
head(tuner_results_bayesian)
#> Epoch setting_id subsample colsample_bytree min_child_weight learning_rate max_depth gpUtility acqOptimum inBounds Elapsed
#> 1: 0 1 0.6 0.8 5 0.2 1 NA FALSE TRUE 1.695
#> 2: 0 2 1.0 0.8 5 0.1 5 NA FALSE TRUE 1.702
#> 3: 0 3 0.8 0.8 5 0.1 1 NA FALSE TRUE 1.734
#> 4: 0 4 0.6 0.8 5 0.2 5 NA FALSE TRUE 1.724
#> 5: 0 5 1.0 0.8 1 0.1 5 NA FALSE TRUE 0.849
#> 6: 0 6 0.8 0.8 5 0.1 5 NA FALSE TRUE 0.850
#> Score metric_optim_mean nrounds errorMessage objective eval_metric
#> 1: -0.4089735 0.4089735 56 NA binary:logistic logloss
#> 2: -0.3970937 0.3970937 49 NA binary:logistic logloss
#> 3: -0.4013240 0.4013240 100 NA binary:logistic logloss
#> 4: -0.4070968 0.4070968 69 NA binary:logistic logloss
#> 5: -0.3819756 0.3819756 39 NA binary:logistic logloss
#> 6: -0.3987643 0.3987643 99 NA binary:logistic logloss
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLCrossValidation$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 0.8799577 1 0.8 1 0.1 5 39 binary:logistic logloss
#> 2: Fold2 0.8635643 1 0.8 1 0.1 5 39 binary:logistic logloss
#> 3: Fold3 0.9027699 1 0.8 1 0.1 5 39 binary:logistic logloss
## -----------------------------------------------------------------------------
validator <- 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 [==================================================================>-----------------------------] 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 [==================================================================>-----------------------------] 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 0.8675304 40 0.6 1 1 0.2 1 binary:logistic logloss
#> 2: Fold2 0.8635643 44 1.0 1 5 0.1 5 binary:logistic logloss
#> 3: Fold3 0.8793103 24 0.6 1 1 0.2 1 binary:logistic logloss
## -----------------------------------------------------------------------------
validator <- 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 0.8662084 0.6 1.0 1 0.2 1 28 binary:logistic logloss
#> 2: Fold2 0.8746695 1.0 0.8 5 0.1 5 44 binary:logistic logloss
#> 3: Fold3 0.8903335 0.6 1.0 1 0.1 5 30 binary:logistic logloss
## -----------------------------------------------------------------------------
preds_xgboost <- mlexperiments::predictions(
object = validator,
newdata = test_x
)
## -----------------------------------------------------------------------------
perf_xgboost <- mlexperiments::performance(
object = validator,
prediction_results = preds_xgboost,
y_ground_truth = test_y,
type = "binary"
)
perf_xgboost
#> model performance auc prauc sensitivity specificity ppv npv tn tp fn fp tnr tpr fnr
#> 1: Fold1 0.7922752 0.7922752 0.6016630 0.5128205 0.8734177 0.6666667 0.7840909 69 20 19 10 0.8734177 0.5128205 0.4871795
#> 2: Fold2 0.7687439 0.7687439 0.5601442 0.3846154 0.8860759 0.6250000 0.7446809 70 15 24 9 0.8860759 0.3846154 0.6153846
#> 3: Fold3 0.7594937 0.7594937 0.6142299 0.4871795 0.8481013 0.6129032 0.7701149 67 19 20 12 0.8481013 0.4871795 0.5128205
#> fpr bbrier acc ce fbeta
#> 1: 0.1265823 0.1726355 0.7542373 0.2457627 0.5797101
#> 2: 0.1139241 0.1885316 0.7203390 0.2796610 0.4761905
#> 3: 0.1518987 0.1854326 0.7288136 0.2711864 0.5428571
## ----include=FALSE------------------------------------------------------------
# nolint end
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