xgboost: Multiclass Classification

    # nolint start
    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("DNA")
    dataset <- DNA |>
      data.table::as.data.table() |>
      na.omit()

    feature_cols <- colnames(dataset)[160:180]
    target_col <- "Class"

## 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" = 4L)
    options("mlexperiments.optim.xgb.nrounds" = 20L)
    options("mlexperiments.optim.xgb.early_stopping_rounds" = 5L)

## 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 = "multi:softprob",
      eval_metric = "mlogloss",
      num_class = 3
    )

    # set arguments for predict function and performance metric,
    # required for mlexperiments::MLCrossValidation and
    # mlexperiments::MLNestedCV
    predict_args <- list(reshape = TRUE)
    performance_metric <- metric("ACC")
    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(
      n_iter = ncores,
      kappa = 3.5,
      acq = "ucb"
    )

## Hyperparameter Tuning

### Grid Search

    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 [============================>-------------------------------------------------------------------] 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
    #>         <int>             <num>   <int>     <num>            <num>            <num>         <num>     <num>         <char>
    #> 1:          1         1.0106675      38       0.6              0.8                5           0.2         1 multi:softprob
    #> 2:          2         0.9828797      37       1.0              0.8                5           0.1         5 multi:softprob
    #> 3:          3         1.0102800      76       0.8              0.8                5           0.1         1 multi:softprob
    #> 4:          4         0.9867769      20       0.6              0.8                5           0.2         5 multi:softprob
    #> 5:          5         0.9815158      32       1.0              0.8                1           0.1         5 multi:softprob
    #> 6:          6         0.9741743      50       0.8              0.8                5           0.1         5 multi:softprob
    #>    eval_metric num_class
    #>         <char>     <num>
    #> 1:    mlogloss         3
    #> 2:    mlogloss         3
    #> 3:    mlogloss         3
    #> 4:    mlogloss         3
    #> 5:    mlogloss         3
    #> 6:    mlogloss         3

### 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
    #>    <num>      <int>     <num>            <num>            <num>         <num>     <num>     <num>     <lgcl>   <lgcl>   <num>
    #> 1:     0          1       0.6              0.8                5           0.2         1        NA      FALSE     TRUE   0.995
    #> 2:     0          2       1.0              0.8                5           0.1         5        NA      FALSE     TRUE   1.057
    #> 3:     0          3       0.8              0.8                5           0.1         1        NA      FALSE     TRUE   1.088
    #> 4:     0          4       0.6              0.8                5           0.2         5        NA      FALSE     TRUE   1.035
    #> 5:     0          5       1.0              0.8                1           0.1         5        NA      FALSE     TRUE   0.282
    #> 6:     0          6       0.8              0.8                5           0.1         5        NA      FALSE     TRUE   0.314
    #>         Score metric_optim_mean nrounds errorMessage      objective eval_metric num_class
    #>         <num>             <num>   <int>       <lgcl>         <char>      <char>     <num>
    #> 1: -1.0106675         1.0106675      38           NA multi:softprob    mlogloss         3
    #> 2: -0.9828797         0.9828797      37           NA multi:softprob    mlogloss         3
    #> 3: -1.0102800         1.0102800      76           NA multi:softprob    mlogloss         3
    #> 4: -0.9867769         0.9867769      20           NA multi:softprob    mlogloss         3
    #> 5: -0.9815158         0.9815158      32           NA multi:softprob    mlogloss         3
    #> 6: -0.9741743         0.9741743      50           NA multi:softprob    mlogloss         3

## 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
    #> CV progress [========================================================================================================] 3/3 (100%)
    #>

    head(validator_results)
    #>      fold performance subsample colsample_bytree min_child_weight learning_rate max_depth nrounds      objective eval_metric
    #>    <char>       <num>     <num>            <num>            <num>         <num>     <num>   <int>         <char>      <char>
    #> 1:  Fold1   0.5685484 0.7220588        0.8330246                1     0.1099728        10      19 multi:softprob    mlogloss
    #> 2:  Fold2   0.5587045 0.7220588        0.8330246                1     0.1099728        10      19 multi:softprob    mlogloss
    #> 3:  Fold3   0.5483871 0.7220588        0.8330246                1     0.1099728        10      19 multi:softprob    mlogloss
    #>    num_class
    #>        <num>
    #> 1:         3
    #> 2:         3
    #> 3:         3

## Nested Cross Validation

### Inner Grid Search

    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 [============================>-------------------------------------------------------------------] 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 [============================>-------------------------------------------------------------------] 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 [============================>-------------------------------------------------------------------] 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 nrounds subsample colsample_bytree min_child_weight learning_rate max_depth      objective eval_metric
    #>    <char>       <num>   <int>     <num>            <num>            <num>         <num>     <num>         <char>      <char>
    #> 1:  Fold1   0.5591398      32       0.6              1.0                1           0.1         5 multi:softprob    mlogloss
    #> 2:  Fold2   0.5344130      34       0.8              0.8                5           0.1         5 multi:softprob    mlogloss
    #> 3:  Fold3   0.5510753      24       0.6              1.0                1           0.1         5 multi:softprob    mlogloss
    #>    num_class
    #>        <num>
    #> 1:         3
    #> 2:         3
    #> 3:         3

### 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
    #>    <char>       <num>     <num>            <num>            <num>         <num>     <num>   <int>         <char>      <char>
    #> 1:  Fold1   0.5591398 0.6000000        1.0000000                1     0.1000000         5      32 multi:softprob    mlogloss
    #> 2:  Fold2   0.5641026 0.6936177        0.7695365                1     0.1099728        10      20 multi:softprob    mlogloss
    #> 3:  Fold3   0.5537634 0.5955781        0.8688622                1     0.1099728        10      19 multi:softprob    mlogloss
    #>    num_class
    #>        <num>
    #> 1:         3
    #> 2:         3
    #> 3:         3

## 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
    )
    perf_xgboost
    #>     model performance
    #>    <char>       <num>
    #> 1:  Fold1   0.5590387
    #> 2:  Fold2   0.5579937
    #> 3:  Fold3   0.5611285

## Appendix I: Grid-Search with Target Weigths

Here, `xgboost`’s `weight`-argument is used to rescale the case-weights during the training.

    # define the target weights
    y_weights <- ifelse(train_y == 1, 0.8, ifelse(train_y == 2, 1.2, 1))
    head(y_weights)
    #> [1] 1.2 1.2 0.0 0.8 0.8 0.0

    tuner_w_weights <- mlexperiments::MLTuneParameters$new(
      learner = mllrnrs::LearnerXgboost$new(
        metric_optimization_higher_better = FALSE
      ),
      strategy = "grid",
      ncores = ncores,
      seed = seed
    )

    tuner_w_weights$parameter_grid <- parameter_grid
    tuner_w_weights$learner_args <- c(
      learner_args,
      list(case_weights = y_weights)
    )
    tuner_w_weights$split_type <- "stratified"

    tuner_w_weights$set_data(
      x = train_x,
      y = train_y
    )

    tuner_results_grid <- tuner_w_weights$execute(k = 3)
    #>
    #> 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
    #>         <int>             <num>   <int>     <num>            <num>            <num>         <num>     <num>         <char>
    #> 1:          1         0.9442324      50       0.6              0.8                5           0.2         1 multi:softprob
    #> 2:          2         0.9217258      35       1.0              0.8                5           0.1         5 multi:softprob
    #> 3:          3         0.9443002      93       0.8              0.8                5           0.1         1 multi:softprob
    #> 4:          4         0.9245540      20       0.6              0.8                5           0.2         5 multi:softprob
    #> 5:          5         0.9212009      26       1.0              0.8                1           0.1         5 multi:softprob
    #> 6:          6         0.9145242      39       0.8              0.8                5           0.1         5 multi:softprob
    #>    eval_metric num_class
    #>         <char>     <num>
    #> 1:    mlogloss         3
    #> 2:    mlogloss         3
    #> 3:    mlogloss         3
    #> 4:    mlogloss         3
    #> 5:    mlogloss         3
    #> 6:    mlogloss         3

## Appendix II: k-Fold Cross Validation with Target Weigths

    validator_w_weights <- mlexperiments::MLCrossValidation$new(
      learner = mllrnrs::LearnerXgboost$new(
        metric_optimization_higher_better = FALSE
      ),
      fold_list = fold_list,
      ncores = ncores,
      seed = seed
    )

    # append the optimized setting from above with the newly created weights
    validator_w_weights$learner_args <- c(
      tuner_w_weights$results$best.setting[-1]
    )

    validator_w_weights$predict_args <- predict_args
    validator_w_weights$performance_metric <- performance_metric
    validator_w_weights$performance_metric_args <- performance_metric_args
    validator_w_weights$return_models <- return_models

    validator_w_weights$set_data(
      x = train_x,
      y = train_y
    )

    validator_results <- validator_w_weights$execute()
    #>
    #> CV fold: Fold1
    #>
    #> CV fold: Fold2
    #>
    #> CV fold: Fold3
    #> CV progress [========================================================================================================] 3/3 (100%)
    #>

    head(validator_results)
    #>      fold performance nrounds subsample colsample_bytree min_child_weight learning_rate max_depth      objective eval_metric
    #>    <char>       <num>   <int>     <num>            <num>            <num>         <num>     <num>         <char>      <char>
    #> 1:  Fold1   0.5658602      39       0.8              0.8                5           0.1         5 multi:softprob    mlogloss
    #> 2:  Fold2   0.5222672      39       0.8              0.8                5           0.1         5 multi:softprob    mlogloss
    #> 3:  Fold3   0.5577957      39       0.8              0.8                5           0.1         5 multi:softprob    mlogloss
    #>    num_class
    #>        <num>
    #> 1:         3
    #> 2:         3
    #> 3:         3