---
title: "glmnet: Multiclass Classification"
vignette: >
  %\VignetteEncoding{UTF-8}
  %\VignetteIndexEntry{glmnet: Multiclass 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_glmnet.R](https://github.com/kapsner/mllrnrs/blob/main/R/learner_glmnet.R) for implementation details.

# Preprocessing

## Import and Prepare Data

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

feature_cols <- colnames(dataset)[160:180]
target_col <- "Class"
```

## 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(
  family = "multinomial",
  type.measure = "class",
  standardize = TRUE
)

# set arguments for predict function and performance metric,
# required for mlexperiments::MLCrossValidation and
# mlexperiments::MLNestedCV
predict_args <- list(type = "response", reshape = TRUE)
performance_metric <- metric("ACC")
performance_metric_args <- NULL
return_models <- FALSE

# required for grid search and initialization of bayesian optimization
parameter_grid <- expand.grid(
  alpha = seq(0, 1, 0.05)
)
# 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(
  alpha = c(0., 1.)
)
optim_args <- list(
  n_iter = ncores,
  kappa = 3.5,
  acq = "ucb"
)
```


## Hyperparameter Tuning

### Grid Search

```{r}
tuner <- mlexperiments::MLTuneParameters$new(
  learner = mllrnrs::LearnerGlmnet$new(
    metric_optimization_higher_better = FALSE
  ),
  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)
#> 
#> Parameter settings [==================>-----------------------------------------------------------------------------] 2/10 ( 20%)
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Parameter settings [===============================================================================================] 10/10 (100%)                                                                                                                                  

head(tuner_results_grid)
#>    setting_id metric_optim_mean      lambda alpha      family type.measure standardize
#> 1:          1         0.4728578 0.003092562  0.70 multinomial        class        TRUE
#> 2:          2         0.4737550 0.002639842  0.90 multinomial        class        TRUE
#> 3:          3         0.4733064 0.003330451  0.65 multinomial        class        TRUE
#> 4:          4         0.4733064 0.017972493  0.10 multinomial        class        TRUE
#> 5:          5         0.4733064 0.003993887  0.45 multinomial        class        TRUE
#> 6:          6         0.4728578 0.022574498  0.05 multinomial        class        TRUE
```


### Bayesian Optimization

```{r}
tuner <- mlexperiments::MLTuneParameters$new(
  learner = mllrnrs::LearnerGlmnet$new(
    metric_optimization_higher_better = FALSE
  ),
  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 alpha gpUtility acqOptimum inBounds Elapsed      Score metric_optim_mean      lambda errorMessage      family
#> 1:     0          1  0.70        NA      FALSE     TRUE   1.666 -0.4728578         0.4728578 0.003092562           NA multinomial
#> 2:     0          2  0.90        NA      FALSE     TRUE   1.635 -0.4737550         0.4737550 0.002639842           NA multinomial
#> 3:     0          3  0.65        NA      FALSE     TRUE   1.657 -0.4733064         0.4733064 0.003330451           NA multinomial
#> 4:     0          4  0.10        NA      FALSE     TRUE   1.744 -0.4733064         0.4733064 0.017972493           NA multinomial
#> 5:     0          5  0.45        NA      FALSE     TRUE   0.577 -0.4733064         0.4733064 0.003993887           NA multinomial
#> 6:     0          6  0.05        NA      FALSE     TRUE   0.687 -0.4728578         0.4728578 0.022574498           NA multinomial
#>    type.measure standardize
#> 1:        class        TRUE
#> 2:        class        TRUE
#> 3:        class        TRUE
#> 4:        class        TRUE
#> 5:        class        TRUE
#> 6:        class        TRUE
```

## k-Fold Cross Validation

```{r}
validator <- mlexperiments::MLCrossValidation$new(
  learner = mllrnrs::LearnerGlmnet$new(
    metric_optimization_higher_better = FALSE
  ),
  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 alpha      lambda      family type.measure standardize
#> 1: Fold1   0.5389785   0.7 0.003092562 multinomial        class        TRUE
#> 2: Fold2   0.5101215   0.7 0.003092562 multinomial        class        TRUE
#> 3: Fold3   0.5309140   0.7 0.003092562 multinomial        class        TRUE
```


## Nested Cross Validation

### Inner Grid Search

```{r}
validator <- mlexperiments::MLNestedCV$new(
  learner = mllrnrs::LearnerGlmnet$new(
    metric_optimization_higher_better = FALSE
  ),
  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
#> 
#> Parameter settings [==================>-----------------------------------------------------------------------------] 2/10 ( 20%)
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Parameter settings [===============================================================================================] 10/10 (100%)                                                                                                                                  
#> CV fold: Fold2
#> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
#> 
#> Parameter settings [==================>-----------------------------------------------------------------------------] 2/10 ( 20%)
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Parameter settings [===============================================================================================] 10/10 (100%)                                                                                                                                  
#> CV fold: Fold3
#> CV progress [========================================================================================================] 3/3 (100%)
#>                                                                                                                                   
#> Parameter settings [==================>-----------------------------------------------------------------------------] 2/10 ( 20%)
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Parameter settings [===============================================================================================] 10/10 (100%)                                                                                                                                  

head(validator_results)
#>     fold performance      lambda alpha      family type.measure standardize
#> 1: Fold1   0.5268817 0.006548214  0.90 multinomial        class        TRUE
#> 2: Fold2   0.4993252 0.001710793  0.65 multinomial        class        TRUE
#> 3: Fold3   0.5268817 0.038236018  0.10 multinomial        class        TRUE
```


### Inner Bayesian Optimization

```{r}
validator <- mlexperiments::MLNestedCV$new(
  learner = mllrnrs::LearnerGlmnet$new(
    metric_optimization_higher_better = FALSE
  ),
  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     alpha      lambda      family type.measure standardize
#> 1: Fold1   0.5389785 0.7000000 0.004817737 multinomial        class        TRUE
#> 2: Fold2   0.5168691 0.4178147 0.017108341 multinomial        class        TRUE
#> 3: Fold3   0.5241935 0.1000000 0.041963982 multinomial        class        TRUE
```


## Holdout Test Dataset Performance

### Predict Outcome in Holdout Test Dataset

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

### Evaluate Performance on Holdout Test Dataset

```{r}
perf_glmnet <- mlexperiments::performance(
  object = validator,
  prediction_results = preds_glmnet,
  y_ground_truth = test_y
)
perf_glmnet
#>    model performance
#> 1: Fold1   0.5339603
#> 2: Fold2   0.5287356
#> 3: Fold3   0.5256008
```

## Appendix I: Grid-Search with Target Weigths

Here, `glmnet`'s [`weights`-argument](https://rdrr.io/cran/glmnet/man/glmnet.html) is used to rescale the case-weights during the training.

```{r}
# define the target weights
y_weights <- ifelse(train_y == "n", 0.8, ifelse(train_y == "ei", 1.2, 1))
head(y_weights)
#> [1] 1.2 1.2 0.0 0.8 0.8 0.0
```

```{r}
tuner_w_weights <- mlexperiments::MLTuneParameters$new(
  learner = mllrnrs::LearnerGlmnet$new(
    metric_optimization_higher_better = FALSE
  ),
  strategy = "grid",
  ncores = ncores,
  seed = seed
)

tuner_w_weights$parameter_grid <- parameter_grid
tuner_w_weights$learner_args <- c(
  learner_args,
  list(case_weights = y_weights)
)
tuner_w_weights$split_type <- "stratified"

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

tuner_results_grid <- tuner_w_weights$execute(k = 3)
#> 
#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
#> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
#> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
#> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
#> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
#> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
#> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
#> Parameter settings [===============================================================================================] 10/10 (100%)                                                                                                                                  

head(tuner_results_grid)
#>    setting_id metric_optim_mean      lambda alpha      family type.measure standardize
#>         <int>             <num>       <num> <num>      <char>       <char>      <lgcl>
#> 1:          1         0.5428029 0.015786209  0.70 multinomial        class        TRUE
#> 2:          2         0.5410926 0.005314924  0.90 multinomial        class        TRUE
#> 3:          3         0.5425178 0.017000533  0.65 multinomial        class        TRUE
#> 4:          4         0.5429929 0.027372552  0.10 multinomial        class        TRUE
#> 5:          5         0.5422328 0.020387093  0.45 multinomial        class        TRUE
#> 6:          6         0.5428979 0.034381521  0.05 multinomial        class        TRUE
```

## Appendix II: k-Fold Cross Validation with Target Weigths

```{r}
validator_w_weights <- mlexperiments::MLCrossValidation$new(
  learner = mllrnrs::LearnerGlmnet$new(
    metric_optimization_higher_better = FALSE
  ),
  fold_list = fold_list,
  ncores = ncores,
  seed = seed
)

# append the optimized setting from above with the newly created weights
validator_w_weights$learner_args <- c(
  tuner_w_weights$results$best.setting[-1]
)

validator_w_weights$predict_args <- predict_args
validator_w_weights$performance_metric <- performance_metric
validator_w_weights$performance_metric_args <- performance_metric_args
validator_w_weights$return_models <- return_models

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

validator_results <- validator_w_weights$execute()
#> 
#> CV fold: Fold1
#> 
#> CV fold: Fold2
#> 
#> CV fold: Fold3
#> CV progress [========================================================================================================] 3/3 (100%)
#>                                                                                                                                   

head(validator_results)
#>      fold performance      lambda alpha      family type.measure standardize
#>    <char>       <num>       <num> <num>      <char>       <char>      <lgcl>
#> 1:  Fold1   0.5456989 0.005314924   0.9 multinomial        class        TRUE
#> 2:  Fold2   0.4736842 0.005314924   0.9 multinomial        class        TRUE
#> 3:  Fold3   0.5040323 0.005314924   0.9 multinomial        class        TRUE
```

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