## ----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" = 4L) options("mlexperiments.optim.xgb.nrounds" = 20L) options("mlexperiments.optim.xgb.early_stopping_rounds" = 5L) ## ----------------------------------------------------------------------------- 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$train, .SD, .SDcols = feature_cols] ) train_y <- as.integer(dataset[data_split$train, get(target_col)]) - 1L test_x <- model.matrix( ~ -1 + ., dataset[data_split$test, .SD, .SDcols = feature_cols] ) test_y <- as.integer(dataset[data_split$test, get(target_col)]) - 1L ## ----------------------------------------------------------------------------- fold_list <- splitTools::create_folds( y = train_y, k = 3, type = "stratified", seed = seed ) ## ----------------------------------------------------------------------------- # required learner arguments, not optimized learner_args <- list( objective = "binary:logistic", eval_metric = "logloss" ) # set arguments for predict function and performance metric, # required for mlexperiments::MLCrossValidation and # mlexperiments::MLNestedCV predict_args <- NULL performance_metric <- metric("auc") performance_metric_args <- list(positive = "1", negative = "0") return_models <- FALSE # required for grid search and initialization of bayesian optimization parameter_grid <- expand.grid( subsample = seq(0.6, 1, .2), colsample_bytree = seq(0.6, 1, .2), min_child_weight = seq(1, 5, 4), learning_rate = seq(0.1, 0.2, 0.1), max_depth = seq(1, 5, 4) ) # 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( subsample = c(0.2, 1), colsample_bytree = c(0.2, 1), min_child_weight = c(1L, 10L), learning_rate = c(0.1, 0.2), max_depth = c(1L, 10L) ) optim_args <- list( n_iter = ncores, kappa = 3.5, acq = "ucb" ) ## ----------------------------------------------------------------------------- tuner <- mlexperiments::MLTuneParameters$new( learner = mllrnrs::LearnerXgboost$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$execute(k = 3) #> #> Parameter settings [==================>-----------------------------------------------------------------------------] 2/10 ( 20%) #> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%) #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%) #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%) #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%) #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%) #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%) #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%) #> Parameter settings [===============================================================================================] 10/10 (100%) head(tuner_results_grid) #> setting_id metric_optim_mean nrounds subsample colsample_bytree min_child_weight learning_rate max_depth objective eval_metric #> #> 1: 1 0.3977489 62 0.6 0.8 5 0.2 1 binary:logistic logloss #> 2: 2 0.3915203 67 1.0 0.8 5 0.1 5 binary:logistic logloss #> 3: 3 0.3972711 96 0.8 0.8 5 0.1 1 binary:logistic logloss #> 4: 4 0.3951791 62 0.6 0.8 5 0.2 5 binary:logistic logloss #> 5: 5 0.3786375 44 1.0 0.8 1 0.1 5 binary:logistic logloss #> 6: 6 0.3956902 75 0.8 0.8 5 0.1 5 binary:logistic logloss ## ----------------------------------------------------------------------------- tuner <- mlexperiments::MLTuneParameters$new( learner = mllrnrs::LearnerXgboost$new( metric_optimization_higher_better = FALSE ), strategy = "bayesian", ncores = ncores, seed = seed ) tuner$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 subsample colsample_bytree min_child_weight learning_rate max_depth gpUtility acqOptimum inBounds Elapsed #> #> 1: 0 1 0.6 0.8 5 0.2 1 NA FALSE TRUE 0.890 #> 2: 0 2 1.0 0.8 5 0.1 5 NA FALSE TRUE 0.892 #> 3: 0 3 0.8 0.8 5 0.1 1 NA FALSE TRUE 0.991 #> 4: 0 4 0.6 0.8 5 0.2 5 NA FALSE TRUE 0.911 #> 5: 0 5 1.0 0.8 1 0.1 5 NA FALSE TRUE 0.173 #> 6: 0 6 0.8 0.8 5 0.1 5 NA FALSE TRUE 0.183 #> Score metric_optim_mean nrounds errorMessage objective eval_metric #> #> 1: -0.3977489 0.3977489 62 NA binary:logistic logloss #> 2: -0.3915203 0.3915203 67 NA binary:logistic logloss #> 3: -0.3972711 0.3972711 96 NA binary:logistic logloss #> 4: -0.3951791 0.3951791 62 NA binary:logistic logloss #> 5: -0.3786375 0.3786375 44 NA binary:logistic logloss #> 6: -0.3956902 0.3956902 75 NA binary:logistic logloss ## ----------------------------------------------------------------------------- validator <- mlexperiments::MLCrossValidation$new( learner = mllrnrs::LearnerXgboost$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$execute() #> #> CV fold: Fold1 #> #> CV fold: Fold2 #> #> CV fold: Fold3 head(validator_results) #> fold performance subsample colsample_bytree min_child_weight learning_rate max_depth nrounds objective eval_metric #> #> 1: Fold1 0.8947647 1 0.8 1 0.1 5 44 binary:logistic logloss #> 2: Fold2 0.8720254 1 0.8 1 0.1 5 44 binary:logistic logloss #> 3: Fold3 0.9010741 1 0.8 1 0.1 5 44 binary:logistic logloss ## ----------------------------------------------------------------------------- validator <- mlexperiments::MLNestedCV$new( learner = mllrnrs::LearnerXgboost$new( metric_optimization_higher_better = FALSE ), strategy = "grid", fold_list = fold_list, k_tuning = 3L, ncores = ncores, seed = seed ) validator$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 [==================================================================>-----------------------------] 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 [==================================================================>-----------------------------] 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 [===============================================>------------------------------------------------] 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 nrounds subsample colsample_bytree min_child_weight learning_rate max_depth objective eval_metric #> #> 1: Fold1 0.8714966 40 0.6 1.0 1 0.2 1 binary:logistic logloss #> 2: Fold2 0.8754627 35 1.0 1.0 5 0.1 5 binary:logistic logloss #> 3: Fold3 0.8883550 41 0.8 0.8 5 0.1 1 binary:logistic logloss ## ----------------------------------------------------------------------------- validator <- mlexperiments::MLNestedCV$new( learner = mllrnrs::LearnerXgboost$new( metric_optimization_higher_better = FALSE ), 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 subsample colsample_bytree min_child_weight learning_rate max_depth nrounds objective eval_metric #> #> 1: Fold1 0.8714966 0.6 1.0000000 1 0.2000000 1 40 binary:logistic logloss #> 2: Fold2 0.8754627 1.0 1.0000000 5 0.1000000 5 35 binary:logistic logloss #> 3: Fold3 0.8810062 1.0 0.6293304 1 0.1034034 1 56 binary:logistic logloss ## ----------------------------------------------------------------------------- preds_xgboost <- mlexperiments::predictions( object = validator, newdata = test_x ) ## ----------------------------------------------------------------------------- perf_xgboost <- mlexperiments::performance( object = validator, prediction_results = preds_xgboost, y_ground_truth = test_y, type = "binary" ) perf_xgboost #> model performance AUC Brier BrierScaled BAC TP TN FP FN TPR TNR FPR FNR #> #> 1: Fold1 0.7913015 0.7913015 0.1743251 0.2121706 0.6994482 20 70 9 19 0.5128205 0.8860759 0.1139241 0.4871795 #> 2: Fold2 0.7745862 0.7745862 0.1856610 0.1609401 0.6481662 16 70 9 23 0.4102564 0.8860759 0.1139241 0.5897436 #> 3: Fold3 0.7917884 0.7917884 0.1739823 0.2137198 0.6609867 17 70 9 22 0.4358974 0.8860759 0.1139241 0.5641026 #> PPV NPV FDR MCC F1 GMEAN GPR ACC MMCE BER #> #> 1: 0.6896552 0.7865169 0.3103448 0.4358249 0.5882353 0.6740904 0.5947010 0.7627119 0.2372881 0.3005518 #> 2: 0.6400000 0.7526882 0.3600000 0.3411249 0.5000000 0.6029248 0.5124101 0.7288136 0.2711864 0.3518338 #> 3: 0.6538462 0.7608696 0.3461538 0.3654140 0.5230769 0.6214807 0.5338631 0.7372881 0.2627119 0.3390133 ## ----include=FALSE------------------------------------------------------------ # nolint end