---
title: "ranger: Binary Classification"
vignette: >
  %\VignetteEncoding{UTF-8}
  %\VignetteIndexEntry{ranger: Binary Classification}
  %\VignetteEngine{quarto::html}
editor_options: 
  chunk_output_type: console
execute:
  eval: false
  collapse: true
  comment: "#>"
---

```{r setup}
# nolint start
library(mlexperiments)
library(mllrnrs)
```

See [https://github.com/kapsner/mllrnrs/blob/main/R/learner_ranger.R](https://github.com/kapsner/mllrnrs/blob/main/R/learner_ranger.R) for implementation details.

# Preprocessing

## Import and Prepare Data

```{r}
library(mlbench)
data("PimaIndiansDiabetes2")
dataset <- PimaIndiansDiabetes2 |>
  data.table::as.data.table() |>
  na.omit()

feature_cols <- colnames(dataset)[1:8]
target_col <- "diabetes"
```

## General Configurations

```{r}
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

```{r}
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

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

# Experiments

## Prepare Experiments

```{r}
# required learner arguments, not optimized
learner_args <- list(probability = TRUE)

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

# required for grid search and initialization of bayesian optimization
parameter_grid <- expand.grid(
  num.trees = seq(500, 1000, 500),
  mtry = seq(2, 6, 2),
  min.node.size = seq(1, 9, 4),
  max.depth = seq(1, 9, 4),
  sample.fraction = seq(0.5, 0.8, 0.3)
)
# 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(
  num.trees = c(100L, 1000L),
  mtry = c(2L, 9L),
  min.node.size = c(1L, 20L),
  max.depth = c(1L, 40L),
  sample.fraction = c(0.3, 1.)
)
optim_args <- list(
  n_iter = ncores,
  kappa = 3.5,
  acq = "ucb"
)
```


## Hyperparameter Tuning

### Grid Search

```{r}
tuner <- mlexperiments::MLTuneParameters$new(
  learner = mllrnrs::LearnerRanger$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.
#> 
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#>  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 num.trees mtry min.node.size max.depth sample.fraction probability
#> 1:          1         0.1750403       500    2             9         5             0.5        TRUE
#> 2:          2         0.1712560       500    2             5         5             0.8        TRUE
#> 3:          3         0.1712560       500    4             9         9             0.5        TRUE
#> 4:          4         0.2335749      1000    2             9         1             0.5        TRUE
#> 5:          5         0.2479871       500    2             9         1             0.8        TRUE
#> 6:          6         0.1859098      1000    6             1         9             0.5        TRUE
```


### Bayesian Optimization

```{r}
tuner <- mlexperiments::MLTuneParameters$new(
  learner = mllrnrs::LearnerRanger$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 num.trees mtry min.node.size max.depth sample.fraction gpUtility acqOptimum inBounds Elapsed      Score
#> 1:     0          1       500    2             9         5             0.5        NA      FALSE     TRUE   1.005 -0.1749597
#> 2:     0          2       500    2             5         5             0.8        NA      FALSE     TRUE   1.008 -0.1748792
#> 3:     0          3       500    4             9         9             0.5        NA      FALSE     TRUE   0.995 -0.1786634
#> 4:     0          4      1000    2             9         1             0.5        NA      FALSE     TRUE   0.987 -0.2407407
#> 5:     0          5       500    2             9         1             0.8        NA      FALSE     TRUE   0.090 -0.2335749
#> 6:     0          6      1000    6             1         9             0.5        NA      FALSE     TRUE   0.332 -0.1785829
#>    metric_optim_mean errorMessage probability
#> 1:         0.1749597         <NA>        TRUE
#> 2:         0.1748792         <NA>        TRUE
#> 3:         0.1786634         <NA>        TRUE
#> 4:         0.2407407         <NA>        TRUE
#> 5:         0.2335749         <NA>        TRUE
#> 6:         0.1785829         <NA>        TRUE
```

## k-Fold Cross Validation

```{r}
validator <- mlexperiments::MLCrossValidation$new(
  learner = mllrnrs::LearnerRanger$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 num.trees mtry min.node.size max.depth sample.fraction probability
#> 1: Fold1   0.8730830      1000    2             9         9             0.5        TRUE
#> 2: Fold2   0.8836594      1000    2             9         9             0.5        TRUE
#> 3: Fold3   0.8937253      1000    2             9         9             0.5        TRUE
```


## Nested Cross Validation

### Inner Grid Search

```{r}
validator <- mlexperiments::MLNestedCV$new(
  learner = mllrnrs::LearnerRanger$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.
#> 
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#>  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.
#> 
#> 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.
#> 
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#>  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.
#> 
#> 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.
#> 
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#>  Classification: using 'classification error rate' as optimization metric.
#> 
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#>  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(validator_results)
#>     fold performance num.trees mtry min.node.size max.depth sample.fraction probability
#> 1: Fold1   0.8714966      1000    6             1         9             0.5        TRUE
#> 2: Fold2   0.8725542       500    4             9         9             0.8        TRUE
#> 3: Fold3   0.8886376       500    2             9         5             0.5        TRUE
```


### Inner Bayesian Optimization

```{r}
validator <- mlexperiments::MLNestedCV$new(
  learner = mllrnrs::LearnerRanger$new(),
  strategy = "bayesian",
  fold_list = fold_list,
  k_tuning = 3L,
  ncores = ncores,
  seed = 312
)

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 num.trees mtry min.node.size max.depth sample.fraction probability
#> 1: Fold1   0.8754627      1000    6             1         9             0.5        TRUE
#> 2: Fold2   0.8767848       500    4             9         9             0.8        TRUE
#> 3: Fold3   0.8971170       500    2             5         9             0.5        TRUE
```


## Holdout Test Dataset Performance

### Predict Outcome in Holdout Test Dataset

```{r}
preds_ranger <- mlexperiments::predictions(
  object = validator,
  newdata = test_x
)
```

### Evaluate Performance on Holdout Test Dataset

```{r}
perf_ranger <- mlexperiments::performance(
  object = validator,
  prediction_results = preds_ranger,
  y_ground_truth = test_y,
  type = "binary"
)
perf_ranger
#>     model performance       AUC     Brier BrierScaled       BAC    TP    TN    FP    FN       TPR       TNR       FPR       FNR       PPV
#>    <char>       <num>     <num>     <num>       <num>     <num> <int> <int> <int> <int>     <num>     <num>     <num>     <num>     <num>
#> 1:  Fold1   0.7874067 0.7874067 0.1735079   0.2158639 0.6548199    18    67    12    21 0.4615385 0.8481013 0.1518987 0.5384615 0.6000000
#> 2:  Fold2   0.7802661 0.7802661 0.1838647   0.1690580 0.6738072    18    70     9    21 0.4615385 0.8860759 0.1139241 0.5384615 0.6666667
#> 3:  Fold3   0.7831873 0.7831873 0.1754549   0.2070644 0.6484907    18    66    13    21 0.4615385 0.8354430 0.1645570 0.5384615 0.5806452
#>          NPV       FDR       MCC        F1     GMEAN       GPR       ACC      MMCE       BER
#>        <num>     <num>     <num>     <num>     <num>     <num>     <num>     <num>     <num>
#> 1: 0.7613636 0.4000000 0.3345034 0.5217391 0.6256448 0.5262348 0.7203390 0.2796610 0.3451801
#> 2: 0.7692308 0.3333333 0.3892611 0.5454545 0.6394983 0.5547002 0.7457627 0.2542373 0.3261928
#> 3: 0.7586207 0.4193548 0.3174204 0.5142857 0.6209582 0.5176776 0.7118644 0.2881356 0.3515093
```

```{r include=FALSE}
# nolint end
```
