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ranger: Binary Classification

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" = 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$train, .SD, .SDcols = feature_cols]
)
train_y <- dataset[data_split$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 <- metric("auc")
performance_metric_args <- list(positive = "pos")
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(
  iters.n = ncores,
  kappa = 3.5,
  acq = "ucb"
)

Hyperparameter Tuning

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 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     prauc sensitivity specificity       ppv       npv tn tp fn fp       tnr       tpr       fnr
#> 1: Fold1   0.7874067 0.7874067 0.6119292   0.4615385   0.8481013 0.6000000 0.7613636 67 18 21 12 0.8481013 0.4615385 0.5384615
#> 2: Fold2   0.7802661 0.7802661 0.5977887   0.4615385   0.8860759 0.6666667 0.7692308 70 18 21  9 0.8860759 0.4615385 0.5384615
#> 3: Fold3   0.7831873 0.7831873 0.6174674   0.4615385   0.8354430 0.5806452 0.7586207 66 18 21 13 0.8354430 0.4615385 0.5384615
#>          fpr    bbrier       acc        ce     fbeta
#> 1: 0.1518987 0.1735079 0.7203390 0.2796610 0.5217391
#> 2: 0.1139241 0.1838647 0.7457627 0.2542373 0.5454545
#> 3: 0.1645570 0.1754549 0.7118644 0.2881356 0.5142857

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.