## ----setup-------------------------------------------------------------------- # nolint start library(mlexperiments) library(mllrnrs) ## ----------------------------------------------------------------------------- library(mlbench) data("DNA") dataset <- DNA |> data.table::as.data.table() |> na.omit() feature_cols <- colnames(dataset)[160:180] target_col <- "Class" ## ----------------------------------------------------------------------------- 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" = 10L) ## ----------------------------------------------------------------------------- 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( 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("bacc") 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( iters.n = ncores, kappa = 3.5, acq = "ucb" ) ## ----------------------------------------------------------------------------- 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$execute(k = 3) #> #> Parameter settings [==================>-----------------------------------------------------------------------------] 2/10 ( 20%) #> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%) #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%) #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%) #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%) #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%) #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%) #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%) #> Parameter settings [===============================================================================================] 10/10 (100%) head(tuner_results_grid) #> setting_id metric_optim_mean lambda alpha family type.measure standardize #> 1: 1 0.4728578 0.003092562 0.70 multinomial class TRUE #> 2: 2 0.4737550 0.002639842 0.90 multinomial class TRUE #> 3: 3 0.4733064 0.003330451 0.65 multinomial class TRUE #> 4: 4 0.4733064 0.017972493 0.10 multinomial class TRUE #> 5: 5 0.4733064 0.003993887 0.45 multinomial class TRUE #> 6: 6 0.4728578 0.022574498 0.05 multinomial class TRUE ## ----------------------------------------------------------------------------- tuner <- mlexperiments::MLTuneParameters$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 ## ----------------------------------------------------------------------------- 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$execute() #> #> CV fold: Fold1 #> #> CV fold: Fold2 #> #> CV fold: Fold3 head(validator_results) #> fold performance alpha lambda family type.measure standardize #> 1: Fold1 0.3672581 0.7 0.003092562 multinomial class TRUE #> 2: Fold2 0.3524351 0.7 0.003092562 multinomial class TRUE #> 3: Fold3 0.3700659 0.7 0.003092562 multinomial class TRUE ## ----------------------------------------------------------------------------- validator <- mlexperiments::MLNestedCV$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 0.3465038 0.006548214 0.90 multinomial class TRUE #> 2: Fold2 0.3475436 0.001710793 0.65 multinomial class TRUE #> 3: Fold3 0.3514970 0.038236018 0.10 multinomial class TRUE ## ----------------------------------------------------------------------------- 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$execute() #> #> CV fold: Fold1 #> #> Registering parallel backend using 4 cores. #> #> CV fold: Fold2 #> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%) #> #> Registering parallel backend using 4 cores. #> #> CV fold: Fold3 #> CV progress [========================================================================================================] 3/3 (100%) #> #> Registering parallel backend using 4 cores. head(validator_results) #> fold performance alpha lambda family type.measure standardize #> 1: Fold1 0.3652609 0.7000000 0.004817737 multinomial class TRUE #> 2: Fold2 0.3416288 0.4178147 0.017108341 multinomial class TRUE #> 3: Fold3 0.3467740 0.1000000 0.041963982 multinomial class TRUE ## ----------------------------------------------------------------------------- preds_glmnet <- mlexperiments::predictions( object = validator, newdata = test_x ) ## ----------------------------------------------------------------------------- perf_glmnet <- mlexperiments::performance( object = validator, prediction_results = preds_glmnet, y_ground_truth = test_y ) perf_glmnet #> model performance #> 1: Fold1 0.3606304 #> 2: Fold2 0.3603913 #> 3: Fold3 0.3529292 ## ----------------------------------------------------------------------------- # define the target weights y_weights <- ifelse(train_y == "n", 0.8, ifelse(train_y == "ei", 1.2, 1)) head(y_weights) #> [1] 1.2 1.2 0.0 0.8 0.8 0.0 ## ----------------------------------------------------------------------------- tuner_w_weights <- mlexperiments::MLTuneParameters$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 #> #> 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 ## ----------------------------------------------------------------------------- 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 #> #> 1: Fold1 0.4075269 0.005314924 0.9 multinomial class TRUE #> 2: Fold2 0.3611115 0.005314924 0.9 multinomial class TRUE #> 3: Fold3 0.3957362 0.005314924 0.9 multinomial class TRUE ## ----include=FALSE------------------------------------------------------------ # nolint end