KNN: Binary Classification

    # nolint start
    library(mlexperiments)

See https://github.com/kapsner/mlexperiments/blob/main/R/learner_knn.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$train, .SD, .SDcols = feature_cols]
    )
    train_y <- as.integer(dataset[data_split$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(
      n_iter = 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