# nolint start
library(mlexperiments)
library(mllrnrs)# nolint start
library(mlexperiments)
library(mllrnrs)See https://github.com/kapsner/mllrnrs/blob/main/R/learner_ranger.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"
cat_vars <- "chas"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 <- data.matrix(
dataset[data_split$train, .SD, .SDcols = feature_cols]
)
train_y <- log(dataset[data_split$train, get(target_col)])
test_x <- data.matrix(
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 <- NULL
# 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(
num.trees = seq(500, 1000, 500),
mtry = seq(2, 6, 2),
min.node.size = seq(1, 9, 4),
max.depth = seq(1, 9, 4),
sample.fraction = seq(0.5, 0.8, 0.3)
)
# 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(
num.trees = c(100L, 1000L),
mtry = c(2L, 9L),
min.node.size = c(1L, 20L),
max.depth = c(1L, 40L),
sample.fraction = c(0.3, 1.)
)
optim_args <- list(
iters.n = ncores,
kappa = 3.5,
acq = "ucb"
)tuner <- mlexperiments::MLTuneParameters$new(
learner = mllrnrs::LearnerRanger$new(),
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,
cat_vars = cat_vars
)
tuner_results_grid <- tuner$execute(k = 3)
#>
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> Regression: using 'mean squared error' as optimization metric.
head(tuner_results_grid)
#> setting_id metric_optim_mean num.trees mtry min.node.size max.depth sample.fraction
#> 1: 1 0.04406585 500 2 9 5 0.5
#> 2: 2 0.03987001 500 2 5 5 0.8
#> 3: 3 0.03405954 500 4 9 9 0.5
#> 4: 4 0.09531892 1000 2 9 1 0.5
#> 5: 5 0.09497929 500 2 9 1 0.8
#> 6: 6 0.03046036 1000 6 1 9 0.5tuner <- mlexperiments::MLTuneParameters$new(
learner = mllrnrs::LearnerRanger$new(),
strategy = "bayesian",
ncores = ncores,
seed = seed
)
tuner$parameter_grid <- parameter_grid
tuner$parameter_bounds <- parameter_bounds
tuner$learner_args <- learner_args
tuner$optim_args <- optim_args
tuner$split_type <- "stratified"
tuner$set_data(
x = train_x,
y = train_y,
cat_vars = cat_vars
)
tuner_results_bayesian <- tuner$execute(k = 3)
#>
#> Registering parallel backend using 4 cores.
head(tuner_results_bayesian)
#> Epoch setting_id num.trees mtry min.node.size max.depth sample.fraction gpUtility acqOptimum inBounds Elapsed Score
#> 1: 0 1 500 2 9 5 0.5 NA FALSE TRUE 1.013 -0.04356188
#> 2: 0 2 500 2 5 5 0.8 NA FALSE TRUE 1.035 -0.03848441
#> 3: 0 3 500 4 9 9 0.5 NA FALSE TRUE 1.064 -0.03375279
#> 4: 0 4 1000 2 9 1 0.5 NA FALSE TRUE 0.994 -0.09582667
#> 5: 0 5 500 2 9 1 0.8 NA FALSE TRUE 0.070 -0.09470805
#> 6: 0 6 1000 6 1 9 0.5 NA FALSE TRUE 0.690 -0.03014795
#> metric_optim_mean errorMessage
#> 1: 0.04356188 NA
#> 2: 0.03848441 NA
#> 3: 0.03375279 NA
#> 4: 0.09582667 NA
#> 5: 0.09470805 NA
#> 6: 0.03014795 NAvalidator <- mlexperiments::MLCrossValidation$new(
learner = mllrnrs::LearnerRanger$new(),
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,
cat_vars = cat_vars
)
validator_results <- validator$execute()
#>
#> CV fold: Fold1
#>
#> CV fold: Fold2
#>
#> CV fold: Fold3
head(validator_results)
#> fold performance num.trees mtry min.node.size max.depth sample.fraction
#> 1: Fold1 0.04028795 100 9 1 9 1
#> 2: Fold2 0.05592193 100 9 1 9 1
#> 3: Fold3 0.04012856 100 9 1 9 1validator <- mlexperiments::MLNestedCV$new(
learner = mllrnrs::LearnerRanger$new(),
strategy = "grid",
fold_list = fold_list,
k_tuning = 3L,
ncores = ncores,
seed = seed
)
validator$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$set_data(
x = train_x,
y = train_y,
cat_vars = cat_vars
)
validator_results <- validator$execute()
#>
#> CV fold: Fold1
#>
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> CV fold: Fold2
#> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
#>
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> CV fold: Fold3
#> CV progress [========================================================================================================] 3/3 (100%)
#>
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Regression: using 'mean squared error' as optimization metric.
#>
#> Parameter settings [===============================================================================================] 10/10 (100%)
#> Regression: using 'mean squared error' as optimization metric.
head(validator_results)
#> fold performance num.trees mtry min.node.size max.depth sample.fraction
#> 1: Fold1 0.0444887 1000 6 1 9 0.5
#> 2: Fold2 0.0481817 500 4 9 9 0.8
#> 3: Fold3 0.0442502 1000 6 1 9 0.5validator <- mlexperiments::MLNestedCV$new(
learner = mllrnrs::LearnerRanger$new(),
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,
cat_vars = cat_vars
)
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 num.trees mtry min.node.size max.depth sample.fraction
#> 1: Fold1 0.04142935 640 7 1 9 0.7460504
#> 2: Fold2 0.05358418 100 9 1 9 1.0000000
#> 3: Fold3 0.04264248 367 4 5 9 0.8388297preds_ranger <- mlexperiments::predictions(
object = validator,
newdata = test_x
)perf_ranger <- mlexperiments::performance(
object = validator,
prediction_results = preds_ranger,
y_ground_truth = test_y,
type = "regression"
)
perf_ranger
#> model performance mse msle mae mape rmse rmsle rsq sse
#> 1: Fold1 0.04145400 0.02627203 0.001718434 0.1125978 0.03799847 0.1620865 0.04145400 0.8291229 4.072165
#> 2: Fold2 0.04849306 0.03319570 0.002351577 0.1270962 0.04379366 0.1821969 0.04849306 0.7840903 5.145334
#> 3: Fold3 0.03827309 0.02222906 0.001464829 0.1067541 0.03631993 0.1490941 0.03827309 0.8554189 3.445504