## ----setup-------------------------------------------------------------------- # nolint start library(mlexperiments) ## ----------------------------------------------------------------------------- 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$train, .SD, .SDcols = feature_cols] ) train_y <- as.integer(dataset[data_split$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( l = 2, test = parse(text = "fold_test$x"), use.all = FALSE ) # set arguments for predict function and performance metric, # required for mlexperiments::MLCrossValidation and # mlexperiments::MLNestedCV predict_args <- list(type = "response") performance_metric <- metric("acc") performance_metric_args <- NULL return_models <- FALSE # required for grid search and initialization of bayesian optimization parameter_grid <- expand.grid( k = seq(4, 68, 6) ) # reduce to a maximum of 10 rows if (nrow(parameter_grid) > 10) { set.seed(123) sample_rows <- sample(seq_len(nrow(parameter_grid)), 10, FALSE) parameter_grid <- kdry::mlh_subset(parameter_grid, sample_rows) } # required for bayesian optimization parameter_bounds <- list(k = c(2L, 80L)) optim_args <- list( iters.n = ncores, kappa = 3.5, acq = "ucb" ) ## ----------------------------------------------------------------------------- tuner <- mlexperiments::MLTuneParameters$new( learner = LearnerKnn$new(), strategy = "grid", ncores = ncores, seed = seed ) tuner$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) head(tuner_results_grid) #> setting_id metric_optim_mean k l use.all #> 1: 1 0.2224638 16 2 FALSE #> 2: 2 0.2628019 64 2 FALSE #> 3: 3 0.2297907 10 2 FALSE #> 4: 4 0.2371981 34 2 FALSE #> 5: 5 0.2627214 58 2 FALSE #> 6: 6 0.2444444 28 2 FALSE ## ----------------------------------------------------------------------------- tuner <- mlexperiments::MLTuneParameters$new( learner = LearnerKnn$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$execute(k = 3) #> Registering parallel backend using 4 cores. head(tuner_results_bayesian) #> Epoch setting_id k gpUtility acqOptimum inBounds Elapsed Score metric_optim_mean errorMessage l use.all #> 1: 0 1 16 NA FALSE TRUE 0.024 -0.2262480 0.2262480 NA 2 FALSE #> 2: 0 2 64 NA FALSE TRUE 0.026 -0.2700483 0.2700483 NA 2 FALSE #> 3: 0 3 10 NA FALSE TRUE 0.023 -0.2370370 0.2370370 NA 2 FALSE #> 4: 0 4 34 NA FALSE TRUE 0.025 -0.2262480 0.2262480 NA 2 FALSE #> 5: 0 5 58 NA FALSE TRUE 0.008 -0.2735910 0.2735910 NA 2 FALSE #> 6: 0 6 28 NA FALSE TRUE 0.006 -0.2589372 0.2589372 NA 2 FALSE ## ----------------------------------------------------------------------------- validator <- mlexperiments::MLCrossValidation$new( learner = LearnerKnn$new(), fold_list = fold_list, ncores = ncores, seed = seed ) validator$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 k l use.all #> 1: Fold1 0.7934783 16 2 FALSE #> 2: Fold2 0.7391304 16 2 FALSE #> 3: Fold3 0.8000000 16 2 FALSE ## ----------------------------------------------------------------------------- validator <- mlexperiments::MLNestedCV$new( learner = LearnerKnn$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$execute() #> #> CV fold: Fold1 #> #> CV fold: Fold2 #> #> CV fold: Fold3 #> CV progress [==========================================================================================================] 3/3 (100%) head(validator_results) #> fold performance k l use.all #> 1: Fold1 0.7391304 22 2 FALSE #> 2: Fold2 0.7391304 28 2 FALSE #> 3: Fold3 0.7666667 34 2 FALSE ## ----------------------------------------------------------------------------- validator <- mlexperiments::MLNestedCV$new( learner = LearnerKnn$new(), strategy = "bayesian", fold_list = fold_list, k_tuning = 3L, ncores = ncores, seed = seed ) validator$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 <- return_models 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 k l use.all #> 1: Fold1 0.7391304 22 2 FALSE #> 2: Fold2 0.7934783 10 2 FALSE #> 3: Fold3 0.7888889 10 2 FALSE ## ----include=FALSE------------------------------------------------------------ # nolint end