rpart: Binary Classification

# nolint start
library(mlexperiments)

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]

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)

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)]

fold_list <- splitTools::create_folds(
  y = train_y,
  k = 3,
  type = "stratified",
  seed = seed
)

# 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"
)

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

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

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

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

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

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.

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

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

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

rpart: Binary Classification

Preprocessing

Import and Prepare Data

General Configurations

Generate Training- and Test Data

Generate Training Data Folds

Experiments

Prepare Experiments

Hyperparameter Tuning

Grid Search

Bayesian Optimization

k-Fold Cross Validation

Nested Cross Validation

Inner Grid Search

Comparison with Logistic Regression

Test Fold Equality

Predict Outcome in Holdout Test Dataset

Evaluate Performance on Holdout Test Dataset