ranger: Regression

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

    feature_cols <- colnames(dataset)[1:13]
    target_col <- "medv"
    cat_vars <- "chas"

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

    test_x <- data.matrix(
      dataset[data_split$test, .SD, .SDcols = feature_cols]
    )
    test_y <- log(dataset[data_split$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 <- NULL

    # 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(
      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,
      cat_vars = cat_vars
    )

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

    head(tuner_results_grid)
    #>    setting_id metric_optim_mean num.trees mtry min.node.size max.depth sample.fraction
    #> 1:          1        0.04406585       500    2             9         5             0.5
    #> 2:          2        0.03987001       500    2             5         5             0.8
    #> 3:          3        0.03405954       500    4             9         9             0.5
    #> 4:          4        0.09531892      1000    2             9         1             0.5
    #> 5:          5        0.09497929       500    2             9         1             0.8
    #> 6:          6        0.03046036      1000    6             1         9             0.5

### 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,
      cat_vars = cat_vars
    )

    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   1.013 -0.04356188
    #> 2:     0          2       500    2             5         5             0.8        NA      FALSE     TRUE   1.035 -0.03848441
    #> 3:     0          3       500    4             9         9             0.5        NA      FALSE     TRUE   1.064 -0.03375279
    #> 4:     0          4      1000    2             9         1             0.5        NA      FALSE     TRUE   0.994 -0.09582667
    #> 5:     0          5       500    2             9         1             0.8        NA      FALSE     TRUE   0.070 -0.09470805
    #> 6:     0          6      1000    6             1         9             0.5        NA      FALSE     TRUE   0.690 -0.03014795
    #>    metric_optim_mean errorMessage
    #> 1:        0.04356188           NA
    #> 2:        0.03848441           NA
    #> 3:        0.03375279           NA
    #> 4:        0.09582667           NA
    #> 5:        0.09470805           NA
    #> 6:        0.03014795           NA

## 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,
      cat_vars = cat_vars
    )

    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
    #> 1: Fold1  0.04028795       100    9             1         9               1
    #> 2: Fold2  0.05592193       100    9             1         9               1
    #> 3: Fold3  0.04012856       100    9             1         9               1

## 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,
      cat_vars = cat_vars
    )

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

    head(validator_results)
    #>     fold performance num.trees mtry min.node.size max.depth sample.fraction
    #> 1: Fold1   0.0444887      1000    6             1         9             0.5
    #> 2: Fold2   0.0481817       500    4             9         9             0.8
    #> 3: Fold3   0.0442502      1000    6             1         9             0.5

### 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,
      cat_vars = cat_vars
    )

    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
    #> 1: Fold1  0.04142935       640    7             1         9       0.7460504
    #> 2: Fold2  0.05358418       100    9             1         9       1.0000000
    #> 3: Fold3  0.04264248       367    4             5         9       0.8388297

## 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 = "regression"
    )
    perf_ranger
    #>     model performance      SSE        MSE      RMSE       MEDSE      SAE       MAE      MEDAE       RSQ    EXPVAR      RRSE       RAE
    #>    <char>       <num>    <num>      <num>     <num>       <num>    <num>     <num>      <num>     <num>     <num>     <num>     <num>
    #> 1:  Fold1  0.04145400 4.072165 0.02627203 0.1620865 0.006378624 17.45267 0.1125978 0.07986629 0.8291229 0.8664940 0.4133729 0.3801520
    #> 2:  Fold2  0.04849306 5.145334 0.03319570 0.1821969 0.007597910 19.69991 0.1270962 0.08716599 0.7840903 1.0172876 0.4646608 0.4291011
    #> 3:  Fold3  0.03827309 3.445504 0.02222906 0.1490941 0.004830835 16.54688 0.1067541 0.06950421 0.8554189 0.7391453 0.3802382 0.3604223
    #>          MAPE KendallTau SpearmanRho
    #>         <num>      <num>       <num>
    #> 1: 0.03799847  0.7618473   0.9175065
    #> 2: 0.04379366  0.7551269   0.9141725
    #> 3: 0.03631993  0.7768003   0.9250850