ranger: Multiclass Classification

    # nolint start
    library(mlexperiments)
    library(mllrnrs)

See https://github.com/kapsner/mllrnrs/blob/main/R/learner_ranger.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)

## 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$train, .SD, .SDcols = feature_cols]
    )
    train_y <- dataset[data_split$train, get(target_col)]

    test_x <- model.matrix(
      ~ -1 + .,
      dataset[data_split$test, .SD, .SDcols = feature_cols]
    )
    test_y <- dataset[data_split$test, get(target_col)]

## 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(probability = TRUE, classification = TRUE)

    # 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(
      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(
      n_iter = ncores,
      kappa = 3.5,
      acq = "ucb"
    )

## Hyperparameter Tuning

### Grid Search

    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
    )

    tuner_results_grid <- tuner$execute(k = 3)
    #>
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [==================>-----------------------------------------------------------------------------] 2/10 ( 20%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [===============================================================================================] 10/10 (100%)
    #>  Classification: using 'classification error rate' as optimization metric.

    head(tuner_results_grid)
    #>    setting_id metric_optim_mean num.trees mtry min.node.size max.depth sample.fraction probability classification
    #> 1:          1         0.4786887       500    2             9         5             0.5        TRUE           TRUE
    #> 2:          2         0.4791386       500    2             5         5             0.8        TRUE           TRUE
    #> 3:          3         0.4419159       500    4             9         9             0.5        TRUE           TRUE
    #> 4:          4         0.4809325      1000    2             9         1             0.5        TRUE           TRUE
    #> 5:          5         0.4809325       500    2             9         1             0.8        TRUE           TRUE
    #> 6:          6         0.4329589      1000    6             1         9             0.5        TRUE           TRUE

### Bayesian Optimization

    tuner <- 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
    )

    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   1.597 -0.4791386
    #> 2:     0          2       500    2             5         5             0.8        NA      FALSE     TRUE   1.641 -0.4786887
    #> 3:     0          3       500    4             9         9             0.5        NA      FALSE     TRUE   2.161 -0.4392295
    #> 4:     0          4      1000    2             9         1             0.5        NA      FALSE     TRUE   1.635 -0.4809325
    #> 5:     0          5       500    2             9         1             0.8        NA      FALSE     TRUE   0.416 -0.4809325
    #> 6:     0          6      1000    6             1         9             0.5        NA      FALSE     TRUE   3.373 -0.4378800
    #>    metric_optim_mean errorMessage probability classification
    #> 1:         0.4791386           NA        TRUE           TRUE
    #> 2:         0.4786887           NA        TRUE           TRUE
    #> 3:         0.4392295           NA        TRUE           TRUE
    #> 4:         0.4809325           NA        TRUE           TRUE
    #> 5:         0.4809325           NA        TRUE           TRUE
    #> 6:         0.4378800           NA        TRUE           TRUE

## k-Fold Cross Validation

    validator <- 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
    )

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

    head(validator_results)
    #>     fold performance num.trees mtry min.node.size max.depth sample.fraction probability classification
    #> 1: Fold1   0.5739247       500    4             9         9             0.8        TRUE           TRUE
    #> 2: Fold2   0.5452092       500    4             9         9             0.8        TRUE           TRUE
    #> 3: Fold3   0.5685484       500    4             9         9             0.8        TRUE           TRUE

## Nested Cross Validation

### Inner Grid Search

    validator <- 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
    )

    validator_results <- validator$execute()
    #>
    #> CV fold: Fold1
    #>
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [===============================================================================================] 10/10 (100%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> CV fold: Fold2
    #> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
    #>
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [===============================================================================================] 10/10 (100%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> CV fold: Fold3
    #> CV progress [========================================================================================================] 3/3 (100%)
    #>
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #> Parameter settings [===============================================================================================] 10/10 (100%)
    #>  Classification: using 'classification error rate' as optimization metric.

    head(validator_results)
    #>     fold performance num.trees mtry min.node.size max.depth sample.fraction probability classification
    #> 1: Fold1   0.5846774      1000    6             1         9             0.5        TRUE           TRUE
    #> 2: Fold2   0.5452092      1000    6             1         9             0.5        TRUE           TRUE
    #> 3: Fold3   0.5712366      1000    6             1         9             0.5        TRUE           TRUE

### Inner Bayesian Optimization

    validator <- 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
    )

    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 probability classification
    #> 1: Fold1   0.5793011      1000    6             1         9       0.5000000        TRUE           TRUE
    #> 2: Fold2   0.5411606       636    6             2        12       0.9378338        TRUE           TRUE
    #> 3: Fold3   0.5725806       388    6             5        14       0.7457303        TRUE           TRUE

## Holdout Test Dataset Performance

### Predict Outcome in Holdout Test Dataset

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

### Evaluate Performance on Holdout Test Dataset

    perf_ranger <- mlexperiments::performance(
      object = validator,
      prediction_results = preds_ranger,
      y_ground_truth = test_y
    )
    perf_ranger
    #>    model performance
    #> 1: Fold1   0.5757576
    #> 2: Fold2   0.5506792
    #> 3: Fold3   0.5726228

## Appendix I: Grid-Search with Target Weigths

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

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

    tuner_w_weights <- mlexperiments::MLTuneParameters$new(
      learner = mllrnrs::LearnerRanger$new(),
      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 num.trees  mtry min.node.size max.depth sample.fraction probability classification
    #>         <int>             <num>     <num> <num>         <num>     <num>           <num>      <lgcl>         <lgcl>
    #> 1:          1         0.4665865       500     2             9         5             0.5        TRUE           TRUE
    #> 2:          2         0.4656941       500     2             5         5             0.8        TRUE           TRUE
    #> 3:          3         0.4486635       500     4             9         9             0.5        TRUE           TRUE
    #> 4:          4         0.4809325      1000     2             9         1             0.5        TRUE           TRUE
    #> 5:          5         0.4809325       500     2             9         1             0.8        TRUE           TRUE
    #> 6:          6         0.4544915      1000     6             1         9             0.5        TRUE           TRUE

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

    validator_w_weights <- mlexperiments::MLCrossValidation$new(
      learner = mllrnrs::LearnerRanger$new(),
      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 num.trees  mtry min.node.size max.depth sample.fraction probability classification
    #>    <char>       <num>     <num> <num>         <num>     <num>           <num>      <lgcl>         <lgcl>
    #> 1:  Fold1   0.5752688       500     2             5         9             0.5        TRUE           TRUE
    #> 2:  Fold2   0.5438596       500     2             5         9             0.5        TRUE           TRUE
    #> 3:  Fold3   0.5564516       500     2             5         9             0.5        TRUE           TRUE