## ----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)
## -----------------------------------------------------------------------------
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(
family = "binomial",
type.measure = "class",
standardize = TRUE
)
# set arguments for predict function and performance metric,
# required for mlexperiments::MLCrossValidation and
# mlexperiments::MLNestedCV
predict_args <- list(type = "response")
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(
alpha = seq(0, 1, 0.05)
)
# 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(
alpha = c(0., 1.)
)
optim_args <- list(
iters.n = ncores,
kappa = 3.5,
acq = "ucb"
)
## -----------------------------------------------------------------------------
tuner <- mlexperiments::MLTuneParameters$new(
learner = mllrnrs::LearnerGlmnet$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$execute(k = 3)
#>
#> Parameter settings [==================>-----------------------------------------------------------------------------] 2/10 ( 20%)
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Parameter settings [===============================================================================================] 10/10 (100%)
head(tuner_results_grid)
#> setting_id metric_optim_mean lambda alpha family type.measure standardize
#> 1: 1 0.1751825 0.094027663 0.70 binomial class TRUE
#> 2: 2 0.1788321 0.080262968 0.90 binomial class TRUE
#> 3: 3 0.1788321 0.101260561 0.65 binomial class TRUE
#> 4: 4 0.1751825 0.006282777 0.10 binomial class TRUE
#> 5: 5 0.1751825 0.110644301 0.45 binomial class TRUE
#> 6: 6 0.1751825 0.006551691 0.05 binomial class TRUE
## -----------------------------------------------------------------------------
tuner <- mlexperiments::MLTuneParameters$new(
learner = mllrnrs::LearnerGlmnet$new(
metric_optimization_higher_better = FALSE
),
strategy = "bayesian",
ncores = ncores,
seed = seed
)
tuner$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 alpha gpUtility acqOptimum inBounds Elapsed Score metric_optim_mean lambda errorMessage family
#> 1: 0 1 0.70 NA FALSE TRUE 0.934 -0.1751825 0.1751825 0.094027663 NA binomial
#> 2: 0 2 0.90 NA FALSE TRUE 0.971 -0.1788321 0.1788321 0.080262968 NA binomial
#> 3: 0 3 0.65 NA FALSE TRUE 0.948 -0.1788321 0.1788321 0.101260561 NA binomial
#> 4: 0 4 0.10 NA FALSE TRUE 0.931 -0.1751825 0.1751825 0.006282777 NA binomial
#> 5: 0 5 0.45 NA FALSE TRUE 0.027 -0.1751825 0.1751825 0.110644301 NA binomial
#> 6: 0 6 0.05 NA FALSE TRUE 0.030 -0.1751825 0.1751825 0.006551691 NA binomial
#> type.measure standardize
#> 1: class TRUE
#> 2: class TRUE
#> 3: class TRUE
#> 4: class TRUE
#> 5: class TRUE
#> 6: class TRUE
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLCrossValidation$new(
learner = mllrnrs::LearnerGlmnet$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$execute()
#>
#> CV fold: Fold1
#>
#> CV fold: Fold2
#>
#> CV fold: Fold3
head(validator_results)
#> fold performance alpha lambda family type.measure standardize
#> 1: Fold1 0.8773136 0.7568403 0.1047508 binomial class TRUE
#> 2: Fold2 0.8630354 0.7568403 0.1047508 binomial class TRUE
#> 3: Fold3 0.8304127 0.7568403 0.1047508 binomial class TRUE
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLNestedCV$new(
learner = mllrnrs::LearnerGlmnet$new(
metric_optimization_higher_better = FALSE
),
strategy = "grid",
fold_list = fold_list,
k_tuning = 3L,
ncores = ncores,
seed = seed
)
validator$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 [==================>-----------------------------------------------------------------------------] 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%)
#> CV fold: Fold2
#> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
#>
#> 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%)
#> CV fold: Fold3
#> CV progress [========================================================================================================] 3/3 (100%)
#>
#> 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(validator_results)
#> fold performance lambda alpha family type.measure standardize
#> 1: Fold1 0.8741407 0.00093823 0.7 binomial class TRUE
#> 2: Fold2 0.8646219 0.09563561 0.7 binomial class TRUE
#> 3: Fold3 0.8648954 0.03175575 0.7 binomial class TRUE
## -----------------------------------------------------------------------------
validator <- mlexperiments::MLNestedCV$new(
learner = mllrnrs::LearnerGlmnet$new(
metric_optimization_higher_better = FALSE
),
strategy = "bayesian",
fold_list = fold_list,
k_tuning = 3L,
ncores = ncores,
seed = 312
)
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 alpha lambda family type.measure standardize
#> 1: Fold1 0.8773136 0.9 0.08390173 binomial class TRUE
#> 2: Fold2 0.8767848 0.1 0.15109601 binomial class TRUE
#> 3: Fold3 0.8507631 0.5 0.11271736 binomial class TRUE
## -----------------------------------------------------------------------------
preds_glmnet <- mlexperiments::predictions(
object = validator,
newdata = test_x
)
## -----------------------------------------------------------------------------
perf_glmnet <- mlexperiments::performance(
object = validator,
prediction_results = preds_glmnet,
y_ground_truth = test_y,
type = "binary"
)
perf_glmnet
#> model performance auc prauc sensitivity specificity ppv npv tn tp fn fp tnr tpr fnr
#> 1: Fold1 0.7656605 0.7656605 0.5841923 0.3333333 0.8860759 0.5909091 0.7291667 70 13 26 9 0.8860759 0.3333333 0.6666667
#> 2: Fold2 0.7831873 0.7831873 0.5822704 0.3846154 0.8860759 0.6250000 0.7446809 70 15 24 9 0.8860759 0.3846154 0.6153846
#> 3: Fold3 0.7627394 0.7627394 0.5747411 0.3589744 0.8607595 0.5600000 0.7311828 68 14 25 11 0.8607595 0.3589744 0.6410256
#> fpr bbrier acc ce fbeta
#> 1: 0.1139241 0.1831706 0.7033898 0.2966102 0.4262295
#> 2: 0.1139241 0.1786208 0.7203390 0.2796610 0.4761905
#> 3: 0.1392405 0.1861673 0.6949153 0.3050847 0.4375000
#>
## ----include=FALSE------------------------------------------------------------
# nolint end