ranger: Binary 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("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" = 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)

    # set arguments for predict function and performance metric,
    # required for mlexperiments::MLCrossValidation and
    # mlexperiments::MLNestedCV
    predict_args <- list(prob = TRUE, positive = "pos")
    performance_metric <- mlexperiments::metric("AUC")
    performance_metric_args <- list(positive = "pos", negative = "neg")
    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.
    #>
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #>  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
    #> 1:          1         0.1750403       500    2             9         5             0.5        TRUE
    #> 2:          2         0.1712560       500    2             5         5             0.8        TRUE
    #> 3:          3         0.1712560       500    4             9         9             0.5        TRUE
    #> 4:          4         0.2335749      1000    2             9         1             0.5        TRUE
    #> 5:          5         0.2479871       500    2             9         1             0.8        TRUE
    #> 6:          6         0.1859098      1000    6             1         9             0.5        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   1.005 -0.1749597
    #> 2:     0          2       500    2             5         5             0.8        NA      FALSE     TRUE   1.008 -0.1748792
    #> 3:     0          3       500    4             9         9             0.5        NA      FALSE     TRUE   0.995 -0.1786634
    #> 4:     0          4      1000    2             9         1             0.5        NA      FALSE     TRUE   0.987 -0.2407407
    #> 5:     0          5       500    2             9         1             0.8        NA      FALSE     TRUE   0.090 -0.2335749
    #> 6:     0          6      1000    6             1         9             0.5        NA      FALSE     TRUE   0.332 -0.1785829
    #>    metric_optim_mean errorMessage probability
    #> 1:         0.1749597         <NA>        TRUE
    #> 2:         0.1748792         <NA>        TRUE
    #> 3:         0.1786634         <NA>        TRUE
    #> 4:         0.2407407         <NA>        TRUE
    #> 5:         0.2335749         <NA>        TRUE
    #> 6:         0.1785829         <NA>        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

    head(validator_results)
    #>     fold performance num.trees mtry min.node.size max.depth sample.fraction probability
    #> 1: Fold1   0.8730830      1000    2             9         9             0.5        TRUE
    #> 2: Fold2   0.8836594      1000    2             9         9             0.5        TRUE
    #> 3: Fold3   0.8937253      1000    2             9         9             0.5        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.
    #>
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #>  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.
    #>
    #>  Classification: using 'classification error rate' as optimization metric.
    #>
    #>  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.
    #>
    #>  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
    #> 1: Fold1   0.8714966      1000    6             1         9             0.5        TRUE
    #> 2: Fold2   0.8725542       500    4             9         9             0.8        TRUE
    #> 3: Fold3   0.8886376       500    2             9         5             0.5        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
    #> 1: Fold1   0.8754627      1000    6             1         9             0.5        TRUE
    #> 2: Fold2   0.8767848       500    4             9         9             0.8        TRUE
    #> 3: Fold3   0.8971170       500    2             5         9             0.5        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,
      type = "binary"
    )
    perf_ranger
    #>     model performance       AUC     Brier BrierScaled       BAC    TP    TN    FP    FN       TPR       TNR       FPR       FNR       PPV
    #>    <char>       <num>     <num>     <num>       <num>     <num> <int> <int> <int> <int>     <num>     <num>     <num>     <num>     <num>
    #> 1:  Fold1   0.7874067 0.7874067 0.1735079   0.2158639 0.6548199    18    67    12    21 0.4615385 0.8481013 0.1518987 0.5384615 0.6000000
    #> 2:  Fold2   0.7802661 0.7802661 0.1838647   0.1690580 0.6738072    18    70     9    21 0.4615385 0.8860759 0.1139241 0.5384615 0.6666667
    #> 3:  Fold3   0.7831873 0.7831873 0.1754549   0.2070644 0.6484907    18    66    13    21 0.4615385 0.8354430 0.1645570 0.5384615 0.5806452
    #>          NPV       FDR       MCC        F1     GMEAN       GPR       ACC      MMCE       BER
    #>        <num>     <num>     <num>     <num>     <num>     <num>     <num>     <num>     <num>
    #> 1: 0.7613636 0.4000000 0.3345034 0.5217391 0.6256448 0.5262348 0.7203390 0.2796610 0.3451801
    #> 2: 0.7692308 0.3333333 0.3892611 0.5454545 0.6394983 0.5547002 0.7457627 0.2542373 0.3261928
    #> 3: 0.7586207 0.4193548 0.3174204 0.5142857 0.6209582 0.5176776 0.7118644 0.2881356 0.3515093