## ----setup-------------------------------------------------------------------- # nolint start library(mlexperiments) library(mllrnrs) ## ----------------------------------------------------------------------------- library(mlbench) data("PimaIndiansDiabetes2") dataset <- PimaIndiansDiabetes2 |> data.table::as.data.table() |> na.omit() feature_cols <- colnames(dataset)[1:8] target_col <- "diabetes" ## ----------------------------------------------------------------------------- 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(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 <- metric("auc") performance_metric_args <- list(positive = "pos") 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( iters.n = ncores, kappa = 3.5, acq = "ucb" ) ## ----------------------------------------------------------------------------- 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 ## ----------------------------------------------------------------------------- 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 TRUE #> 2: 0.1748792 TRUE #> 3: 0.1786634 TRUE #> 4: 0.2407407 TRUE #> 5: 0.2335749 TRUE #> 6: 0.1785829 TRUE ## ----------------------------------------------------------------------------- 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$execute() #> #> CV fold: Fold1 #> #> CV fold: Fold2 #> #> CV fold: Fold3 head(validator_results) #> fold performance num.trees mtry min.node.size max.depth sample.fraction probability #> 1: Fold1 0.8730830 1000 2 9 9 0.5 TRUE #> 2: Fold2 0.8836594 1000 2 9 9 0.5 TRUE #> 3: Fold3 0.8937253 1000 2 9 9 0.5 TRUE ## ----------------------------------------------------------------------------- validator <- mlexperiments::MLNestedCV$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 ## ----------------------------------------------------------------------------- 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 ## ----------------------------------------------------------------------------- preds_ranger <- mlexperiments::predictions( object = validator, newdata = test_x ) ## ----------------------------------------------------------------------------- perf_ranger <- mlexperiments::performance( object = validator, prediction_results = preds_ranger, y_ground_truth = test_y, type = "binary" ) perf_ranger #> model performance auc prauc sensitivity specificity ppv npv tn tp fn fp tnr tpr fnr #> 1: Fold1 0.7874067 0.7874067 0.6119292 0.4615385 0.8481013 0.6000000 0.7613636 67 18 21 12 0.8481013 0.4615385 0.5384615 #> 2: Fold2 0.7802661 0.7802661 0.5977887 0.4615385 0.8860759 0.6666667 0.7692308 70 18 21 9 0.8860759 0.4615385 0.5384615 #> 3: Fold3 0.7831873 0.7831873 0.6174674 0.4615385 0.8354430 0.5806452 0.7586207 66 18 21 13 0.8354430 0.4615385 0.5384615 #> fpr bbrier acc ce fbeta #> 1: 0.1518987 0.1735079 0.7203390 0.2796610 0.5217391 #> 2: 0.1139241 0.1838647 0.7457627 0.2542373 0.5454545 #> 3: 0.1645570 0.1754549 0.7118644 0.2881356 0.5142857 ## ----include=FALSE------------------------------------------------------------ # nolint end