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KNN: Binary Classification

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$train, .SD, .SDcols = feature_cols]
)
train_y <- as.integer(dataset[data_split$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(
  l = 2,
  test = parse(text = "fold_test$x"),
  use.all = FALSE
)

# set arguments for predict function and performance metric,
# required for mlexperiments::MLCrossValidation and
# mlexperiments::MLNestedCV
predict_args <- list(type = "response")
performance_metric <- metric("acc")
performance_metric_args <- NULL
return_models <- FALSE

# required for grid search and initialization of bayesian optimization
parameter_grid <- expand.grid(
  k = seq(4, 68, 6)
)
# 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(k = c(2L, 80L))
optim_args <- list(
  iters.n = ncores,
  kappa = 3.5,
  acq = "ucb"
)

Hyperparameter Tuning

Grid Search

tuner <- mlexperiments::MLTuneParameters$new(
  learner = LearnerKnn$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)

head(tuner_results_grid)
#>    setting_id metric_optim_mean  k l use.all
#> 1:          1         0.2224638 16 2   FALSE
#> 2:          2         0.2628019 64 2   FALSE
#> 3:          3         0.2297907 10 2   FALSE
#> 4:          4         0.2371981 34 2   FALSE
#> 5:          5         0.2627214 58 2   FALSE
#> 6:          6         0.2444444 28 2   FALSE

Bayesian Optimization

tuner <- mlexperiments::MLTuneParameters$new(
  learner = LearnerKnn$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  k gpUtility acqOptimum inBounds Elapsed      Score metric_optim_mean errorMessage l use.all
#> 1:     0          1 16        NA      FALSE     TRUE   0.024 -0.2262480         0.2262480           NA 2   FALSE
#> 2:     0          2 64        NA      FALSE     TRUE   0.026 -0.2700483         0.2700483           NA 2   FALSE
#> 3:     0          3 10        NA      FALSE     TRUE   0.023 -0.2370370         0.2370370           NA 2   FALSE
#> 4:     0          4 34        NA      FALSE     TRUE   0.025 -0.2262480         0.2262480           NA 2   FALSE
#> 5:     0          5 58        NA      FALSE     TRUE   0.008 -0.2735910         0.2735910           NA 2   FALSE
#> 6:     0          6 28        NA      FALSE     TRUE   0.006 -0.2589372         0.2589372           NA 2   FALSE

k-Fold Cross Validation

validator <- mlexperiments::MLCrossValidation$new(
  learner = LearnerKnn$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  k l use.all
#> 1: Fold1   0.7934783 16 2   FALSE
#> 2: Fold2   0.7391304 16 2   FALSE
#> 3: Fold3   0.8000000 16 2   FALSE

Nested Cross Validation

Inner Grid Search

validator <- mlexperiments::MLNestedCV$new(
  learner = LearnerKnn$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
#> 
#> CV fold: Fold2
#> 
#> CV fold: Fold3
#> CV progress [==========================================================================================================] 3/3 (100%)

head(validator_results)
#>     fold performance  k l use.all
#> 1: Fold1   0.7391304 22 2   FALSE
#> 2: Fold2   0.7391304 28 2   FALSE
#> 3: Fold3   0.7666667 34 2   FALSE

Inner Bayesian Optimization

validator <- mlexperiments::MLNestedCV$new(
  learner = LearnerKnn$new(),
  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 <- return_models

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  k l use.all
#> 1: Fold1   0.7391304 22 2   FALSE
#> 2: Fold2   0.7934783 10 2   FALSE
#> 3: Fold3   0.7888889 10 2   FALSE

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