# 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("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 3.417052These 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.