rpart: Binary Classification

    # nolint start
    library(mlexperiments)

See https://github.com/kapsner/mlexperiments/blob/main/R/learner_rpart.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$train, .SD, .SDcols = feature_cols]
    )
    train_y <- dataset[data_split$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(method = "class")

    # set arguments for predict function and performance metric,
    # required for mlexperiments::MLCrossValidation and
    # mlexperiments::MLNestedCV
    predict_args <- list(type = "prob")
    performance_metric <- metric("AUC")
    performance_metric_args <- list(
      positive = "malignant",
      negative = "benign"
    )
    return_models <- FALSE

    # required for grid search and initialization of bayesian optimization
    parameter_grid <- expand.grid(
      minsplit = seq(2L, 10L, 1L),
      cp = seq(0.01, 0.1, 0.01),
      maxdepth = seq(2L, 10L, 2L)
    )
    # 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(
      minsplit = c(2L, 10L),
      cp = c(0.01, 0.1),
      maxdepth = c(2L, 10L)
    )
    optim_args <- list(
      n_iter = ncores,
      kappa = 3.5,
      acq = "ucb"
    )

## Hyperparameter Tuning

### Grid Search

    tuner <- mlexperiments::MLTuneParameters$new(
      learner = LearnerRpart$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)
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #> Parameter settings [===============================================================================================] 10/10 (100%)
    #>  Classification: using 'mean misclassification error' as optimization metric.

    head(tuner_results_grid)
    #>    setting_id metric_optim_mean minsplit    cp maxdepth method
    #>         <int>             <num>    <int> <num>    <int> <char>
    #> 1:          1        0.04823229        2  0.07       10  class
    #> 2:          2        0.04823229        9  0.10        4  class
    #> 3:          3        0.06698229        6  0.02        2  class
    #> 4:          4        0.04823229        7  0.02        6  class
    #> 5:          5        0.04823229        4  0.08       10  class
    #> 6:          6        0.04823229       10  0.04        8  class

### Bayesian Optimization

    tuner <- mlexperiments::MLTuneParameters$new(
      learner = LearnerRpart$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.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #> elapsed = 0.121  Round = 1   minsplit = 6.0000   cp = 0.0200 maxdepth = 2.0000   Value = -0.06698229
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #> elapsed = 0.13   Round = 2   minsplit = 2.0000   cp = 0.0800 maxdepth = 6.0000   Value = -0.04823229
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #> elapsed = 0.131  Round = 3   minsplit = 9.0000   cp = 0.1000 maxdepth = 4.0000   Value = -0.04823229
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #> elapsed = 0.036  Round = 4   minsplit = 3.0000   cp = 0.0400 maxdepth = 8.0000   Value = -0.04823229
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #> elapsed = 0.136  Round = 5   minsplit = 10.0000  cp = 0.1000 maxdepth = 10.0000  Value = -0.04823229
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #> elapsed = 0.122  Round = 6   minsplit = 7.0000   cp = 0.0810495  maxdepth = 8.0000   Value = -0.04823229
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #> elapsed = 0.155  Round = 7   minsplit = 4.0000   cp = 0.05388637 maxdepth = 2.0000   Value = -0.06698229
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #> elapsed = 0.10   Round = 8   minsplit = 8.0000   cp = 0.03326189 maxdepth = 9.0000   Value = -0.04823229
    #>
    #>  Best Parameters Found:
    #> Round = 2    minsplit = 2.0000   cp = 0.0800 maxdepth = 6.0000   Value = -0.04823229

    head(tuner_results_bayesian)
    #>    setting_id minsplit        cp maxdepth       Value method metric_optim_mean
    #>         <int>    <num>     <num>    <num>       <num> <char>             <num>
    #> 1:          1        6 0.0200000        2 -0.06698229  class        0.06698229
    #> 2:          2        2 0.0800000        6 -0.04823229  class        0.04823229
    #> 3:          3        9 0.1000000        4 -0.04823229  class        0.04823229
    #> 4:          4        3 0.0400000        8 -0.04823229  class        0.04823229
    #> 5:          5       10 0.1000000       10 -0.04823229  class        0.04823229
    #> 6:          6        7 0.0810495        8 -0.04823229  class        0.04823229

## k-Fold Cross Validation

    validator <- mlexperiments::MLCrossValidation$new(
      learner = LearnerRpart$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    cp maxdepth method
    #>    <char>       <num> <num>    <num> <char>
    #> 1:  Fold1   0.9613416  0.08        6  class
    #> 2:  Fold2   0.9230236  0.08        6  class
    #> 3:  Fold3   0.9751030  0.08        6  class

## Nested Cross Validation

### Inner Grid Search

    validator <- mlexperiments::MLNestedCV$new(
      learner = LearnerRpart$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 <- TRUE

    validator$set_data(
      x = train_x,
      y = train_y
    )

    validator_results <- validator$execute()
    #>
    #> CV fold: Fold1
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #> Parameter settings [====================================================================================================] 10/10 (100%)
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #> CV fold: Fold2
    #> CV progress [========================================================================>------------------------------------] 2/3 ( 67%)
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #> Parameter settings [==========================================================================================>----------] 9/10 ( 90%)
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #> Parameter settings [====================================================================================================] 10/10 (100%)
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #> CV fold: Fold3
    #> CV progress [=============================================================================================================] 3/3 (100%)
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #>  Classification: using 'mean misclassification error' as optimization metric.
    #>
    #> Parameter settings [====================================================================================================] 10/10 (100%)
    #>  Classification: using 'mean misclassification error' as optimization metric.

    head(validator_results)
    #>      fold performance minsplit    cp maxdepth method
    #>    <char>       <num>    <int> <num>    <int> <char>
    #> 1:  Fold1   0.9613416        6  0.02        2  class
    #> 2:  Fold2   0.9230236        2  0.07       10  class
    #> 3:  Fold3   0.9751030        2  0.07       10  class

## Comparison with Logistic Regression

See https://github.com/kapsner/mlexperiments/blob/main/R/learner_glm.R for implementation details.

    validator_glm <- mlexperiments::MLCrossValidation$new(
      learner = LearnerGlm$new(),
      fold_list = fold_list,
      ncores = ncores,
      seed = seed
    )

    validator_glm$learner_args <- list(family = binomial(link = "logit"))
    validator_glm$predict_args <- list(type = "response")
    validator_glm$performance_metric <- performance_metric
    validator_glm$performance_metric_args <- performance_metric_args
    validator_glm$return_models <- TRUE

    validator_glm$set_data(
      x = train_x,
      y = train_y
    )

    validator_glm_results <- validator_glm$execute()
    #>
    #> CV fold: Fold1
    #> Parameter 'ncores' is ignored for learner 'LearnerGlm'.
    #> Warning: glm.fit: algorithm did not converge
    #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    #> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == : prediction from rank-deficient fit; attr(*, "non-estim")
    #> has doubtful cases
    #>
    #> CV fold: Fold2
    #> Parameter 'ncores' is ignored for learner 'LearnerGlm'.
    #> Warning: glm.fit: algorithm did not converge
    #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    #>
    #> CV fold: Fold3
    #> CV progress [=============================================================================================================] 3/3 (100%)
    #>                                                                                                                                        Parameter 'ncores' is ignored for learner 'LearnerGlm'.
    #> Warning: glm.fit: algorithm did not converge
    #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

    head(validator_glm_results)
    #>      fold performance
    #>    <char>       <num>
    #> 1:  Fold1   0.9239188
    #> 2:  Fold2   0.8978849
    #> 3:  Fold3   0.9184409

### Test Fold Equality

    mlexperiments::validate_fold_equality(
      experiments = list(validator, validator_glm)
    )
    #>
    #> Testing for identical folds in 1 and 2.
    #>
    #> Testing for identical folds in 2 and 1.

### Predict Outcome in Holdout Test Dataset

    preds_rpart <- mlexperiments::predictions(
      object = validator,
      newdata = test_x
    )

    preds_glm <- mlexperiments::predictions(
      object = validator_glm,
      newdata = test_x
    )

### Evaluate Performance on Holdout Test Dataset

    perf_rpart <- mlexperiments::performance(
      object = validator,
      prediction_results = preds_rpart,
      y_ground_truth = test_y,
      type = "binary"
    )

    perf_glm <- mlexperiments::performance(
      object = validator_glm,
      prediction_results = preds_glm,
      y_ground_truth = test_y,
      type = "binary"
    )

    # combine results for plotting
    final_results <- rbind(
      cbind(algorithm = "rpart", perf_rpart),
      cbind(algorithm = "glm", perf_glm)
    )

    # p <- ggpubr::ggdotchart(
    #   data = final_results,
    #   x = "algorithm",
    #   y = "AUC",
    #   color = "model",
    #   rotate = TRUE
    # )
    # p

Model Comparison