The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

xgboost: Binary Classification

library(mlexperiments)
library(mllrnrs)

See https://github.com/kapsner/mllrnrs/blob/main/R/learner_xgboost.R for implementation details.

Preprocessing

Import and Prepare Data

library(mlbench)
data("PimaIndiansDiabetes2")
dataset <- PimaIndiansDiabetes2 |>
  data.table::as.data.table() |>
  na.omit()

feature_cols <- colnames(dataset)[1:8]
target_col <- "diabetes"

General Configurations

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)

Generate Training- and Test Data

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

Generate Training Data Folds

fold_list <- splitTools::create_folds(
  y = train_y,
  k = 3,
  type = "stratified",
  seed = seed
)

Experiments

Prepare Experiments

# 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"
)

Hyperparameter Tuning

Bayesian Optimization

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

k-Fold Cross Validation

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

Nested Cross Validation

Inner Bayesian Optimization

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

Holdout Test Dataset Performance

Predict Outcome in Holdout Test Dataset

preds_xgboost <- mlexperiments::predictions(
  object = validator,
  newdata = test_x
)

Evaluate Performance on Holdout Test Dataset

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

These 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.
Health stats visible at Monitor.