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 = "pos")
return_models <- FALSE

# required for grid search and initialization of bayesian optimization
parameter_grid <- expand.grid(
  minsplit = seq(2L, 82L, 10L),
  cp = seq(0.01, 0.1, 0.01),
  maxdepth = seq(2L, 30L, 5L)
)
# 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, 100L),
  cp = c(0.01, 0.1),
  maxdepth = c(2L, 30L)
)
optim_args <- list(
  iters.n = 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 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#> Parameter settings [=======================================================================================>----------] 9/10 ( 90%)
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#> Parameter settings [=================================================================================================] 10/10 (100%)                                                                                                                                    
#>  Classification: using 'classification error rate' as optimization metric.

head(tuner_results_grid)
#>    setting_id metric_optim_mean minsplit   cp maxdepth method
#> 1:          1         0.1860709        2 0.07       22  class
#> 2:          2         0.1860709       32 0.02       27  class
#> 3:          3         0.1860709       72 0.10        7  class
#> 4:          4         0.1860709       32 0.09       27  class
#> 5:          5         0.1860709       52 0.02       12  class
#> 6:          6         0.1860709        2 0.04        7  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)
#> 
#> Registering parallel backend using 4 cores.

head(tuner_results_bayesian)
#>    Epoch setting_id minsplit   cp maxdepth gpUtility acqOptimum inBounds Elapsed      Score metric_optim_mean errorMessage method
#> 1:     0          1        2 0.07       22        NA      FALSE     TRUE   0.044 -0.1860709         0.1860709           NA  class
#> 2:     0          2       32 0.02       27        NA      FALSE     TRUE   0.044 -0.1860709         0.1860709           NA  class
#> 3:     0          3       72 0.10        7        NA      FALSE     TRUE   0.044 -0.1860709         0.1860709           NA  class
#> 4:     0          4       32 0.09       27        NA      FALSE     TRUE   0.044 -0.1860709         0.1860709           NA  class
#> 5:     0          5       52 0.02       12        NA      FALSE     TRUE   0.020 -0.1860709         0.1860709           NA  class
#> 6:     0          6        2 0.04        7        NA      FALSE     TRUE   0.021 -0.1860709         0.1860709           NA  class

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 minsplit   cp maxdepth method
#> 1: Fold1   0.8323638        2 0.07       22  class
#> 2: Fold2   0.7342676        2 0.07       22  class
#> 3: Fold3   0.7959299        2 0.07       22  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 <- return_models

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

validator_results <- validator$execute()
#> 
#> CV fold: Fold1
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#> CV fold: Fold2
#> CV progress [======================================================================>-----------------------------------] 2/3 ( 67%)
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#> CV fold: Fold3
#> CV progress [==========================================================================================================] 3/3 (100%)
#>                                                                                                                                     
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#>  Classification: using 'classification error rate' as optimization metric.

head(validator_results)
#>     fold performance minsplit   cp maxdepth method
#> 1: Fold1   0.7496034       42 0.02        2  class
#> 2: Fold2   0.6845584       42 0.02        2  class
#> 3: Fold3   0.7959299        2 0.07       22  class

Inner Bayesian Optimization

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

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 minsplit   cp maxdepth method
#> 1: Fold1   0.7496034       42 0.02        2  class
#> 2: Fold2   0.6845584       42 0.02        2  class
#> 3: Fold3   0.7959299        2 0.07       22  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'.
#> 
#> CV fold: Fold2
#> Parameter 'ncores' is ignored for learner 'LearnerGlm'.
#> 
#> CV fold: Fold3
#> Parameter 'ncores' is ignored for learner 'LearnerGlm'.

head(validator_glm_results)
#>     fold performance
#> 1: Fold1   0.8746695
#> 2: Fold2   0.8751983
#> 3: Fold3   0.8801583

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

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

Inner Bayesian Optimization

Comparison with Logistic Regression

Test Fold Equality

Predict Outcome in Holdout Test Dataset

Evaluate Performance on Holdout Test Dataset