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

    seed <- 123
    feature_cols <- colnames(dataset)[2:10]
    target_col <- "Class"
    to_num <- c(
      "Cl.thickness",
      "Cell.size",
      "Cell.shape",
      "Marg.adhesion",
      "Epith.c.size"
    )
    dataset[, (to_num) := lapply(.SD, as.numeric), .SDcols = to_num]

## 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
    #>         <int>             <num> <num> <num>  <lgcl>
    #> 1:          1        0.03979364    16     2   FALSE
    #> 2:          2        0.06281546    64     2   FALSE
    #> 3:          3        0.03771031    10     2   FALSE
    #> 4:          4        0.04393394    34     2   FALSE
    #> 5:          5        0.05862242    58     2   FALSE
    #> 6:          6        0.04393394    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)
    #>
    #> Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
    #> ... reducing initialization grid to 4 rows.
    #> elapsed = 0.016  Round = 1   k = 10.0000 Value = -0.03771031
    #> elapsed = 0.02   Round = 2   k = 4.0000  Value = -0.04607001
    #> elapsed = 0.014  Round = 3   k = 64.0000 Value = -0.06281546
    #> elapsed = 0.013  Round = 4   k = 52.0000 Value = -0.06070576
    #> elapsed = 0.012  Round = 5   k = 25.0000 Value = -0.0418506
    #> elapsed = 0.015  Round = 6   k = 80.0000 Value = -0.05860932
    #> elapsed = 0.011  Round = 7   k = 17.0000 Value = -0.03976727
    #> elapsed = 0.013  Round = 8   k = 37.0000 Value = -0.04811371
    #>
    #>  Best Parameters Found:
    #> Round = 1    k = 10.0000 Value = -0.03771031

    head(tuner_results_bayesian)
    #>    setting_id     k       Value     l use.all metric_optim_mean
    #>         <int> <num>       <num> <num>  <lgcl>             <num>
    #> 1:          1    10 -0.03771031     2   FALSE        0.03771031
    #> 2:          2     4 -0.04607001     2   FALSE        0.04607001
    #> 3:          3    64 -0.06281546     2   FALSE        0.06281546
    #> 4:          4    52 -0.06070576     2   FALSE        0.06070576
    #> 5:          5    25 -0.04185060     2   FALSE        0.04185060
    #> 6:          6    80 -0.05860932     2   FALSE        0.05860932

## 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

    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
    #>    <char>       <num> <num> <num>  <lgcl>
    #> 1:  Fold1   0.9746835    10     2   FALSE
    #> 2:  Fold2   0.9496855    10     2   FALSE
    #> 3:  Fold3   0.9625000    10     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
    #>    <char>       <num> <num> <num>  <lgcl>
    #> 1:  Fold1   0.9746835    10     2   FALSE
    #> 2:  Fold2   0.9496855    10     2   FALSE
    #> 3:  Fold3   0.9625000    10     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
    #>
    #> Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
    #> ... reducing initialization grid to 4 rows.
    #> elapsed = 0.009  Round = 1   k = 10.0000 Value = -0.04699348
    #> elapsed = 0.008  Round = 2   k = 4.0000  Value = -0.05639805
    #> elapsed = 0.01   Round = 3   k = 64.0000 Value = -0.07523658
    #> elapsed = 0.01   Round = 4   k = 52.0000 Value = -0.07209193
    #> elapsed = 0.009  Round = 5   k = 25.0000 Value = -0.05331217
    #> elapsed = 0.011  Round = 6   k = 80.0000 Value = -0.08152589
    #> elapsed = 0.009  Round = 7   k = 17.0000 Value = -0.05016752
    #> elapsed = 0.011  Round = 8   k = 37.0000 Value = -0.06271675
    #>
    #>  Best Parameters Found:
    #> Round = 1    k = 10.0000 Value = -0.04699348
    #>
    #> CV fold: Fold2
    #> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
    #>
    #> Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
    #> ... reducing initialization grid to 4 rows.
    #> elapsed = 0.009  Round = 1   k = 10.0000 Value = -0.02830189
    #> elapsed = 0.009  Round = 2   k = 4.0000  Value = -0.0408805
    #> elapsed = 0.01   Round = 3   k = 64.0000 Value = -0.07232704
    #> elapsed = 0.01   Round = 4   k = 52.0000 Value = -0.05974843
    #> elapsed = 0.01   Round = 5   k = 25.0000 Value = -0.03773585
    #> elapsed = 0.01   Round = 6   k = 80.0000 Value = -0.08490566
    #> elapsed = 0.01   Round = 7   k = 17.0000 Value = -0.02830189
    #> elapsed = 0.01   Round = 8   k = 39.0000 Value = -0.05031447
    #>
    #>  Best Parameters Found:
    #> Round = 1    k = 10.0000 Value = -0.02830189
    #>
    #> CV fold: Fold3
    #> CV progress [========================================================================================================] 3/3 (100%)
    #>
    #> Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
    #> ... reducing initialization grid to 4 rows.
    #> elapsed = 0.01   Round = 1   k = 10.0000 Value = -0.04117999
    #> elapsed = 0.008  Round = 2   k = 4.0000  Value = -0.0442947
    #> elapsed = 0.01   Round = 3   k = 64.0000 Value = -0.06004792
    #> elapsed = 0.01   Round = 4   k = 52.0000 Value = -0.05687332
    #> elapsed = 0.009  Round = 5   k = 27.0000 Value = -0.04432465
    #> elapsed = 0.009  Round = 6   k = 17.0000 Value = -0.04432465
    #> elapsed = 0.011  Round = 7   k = 80.0000 Value = -0.06951183
    #> elapsed = 0.01   Round = 8   k = 38.0000 Value = -0.05372866
    #>
    #>  Best Parameters Found:
    #> Round = 1    k = 10.0000 Value = -0.04117999

    head(validator_results)
    #>      fold performance     k     l use.all
    #>    <char>       <num> <num> <num>  <lgcl>
    #> 1:  Fold1   0.9746835    10     2   FALSE
    #> 2:  Fold2   0.9496855    10     2   FALSE
    #> 3:  Fold3   0.9625000    10     2   FALSE