## ----setup-------------------------------------------------------------------- # nolint start library(mlexperiments) library(mllrnrs) ## ----------------------------------------------------------------------------- library(mlbench) data("DNA") dataset <- DNA |> data.table::as.data.table() |> na.omit() feature_cols <- colnames(dataset)[1:180] 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) options("mlexperiments.optim.lgb.nrounds" = 100L) options("mlexperiments.optim.lgb.early_stopping_rounds" = 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( max_depth = -1L, verbose = -1L, objective = "multiclass", metric = "multi_logloss", num_class = "3" ) # set arguments for predict function and performance metric, # required for mlexperiments::MLCrossValidation and # mlexperiments::MLNestedCV predict_args <- list(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( bagging_fraction = seq(0.6, 1, .2), feature_fraction = seq(0.6, 1, .2), min_data_in_leaf = seq(2, 10, 2), learning_rate = seq(0.1, 0.2, 0.1), num_leaves = seq(2, 20, 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( bagging_fraction = c(0.2, 1), feature_fraction = c(0.2, 1), min_data_in_leaf = c(2L, 12L), learning_rate = c(0.1, 0.2), num_leaves = c(2L, 20L) ) optim_args <- list( iters.n = ncores, kappa = 3.5, acq = "ucb" ) ## ----------------------------------------------------------------------------- tuner <- mlexperiments::MLTuneParameters$new( learner = mllrnrs::LearnerLightgbm$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) #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -0.656204 #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> #> Parameter settings [==================>-----------------------------------------------------------------------------] 2/10 ( 20%) #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -0.656204 #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> #> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%) #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -0.656204 #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%) #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -0.656204 #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%) #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -0.656204 #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%) #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -0.656204 #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%) #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -0.656204 #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%) #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -0.656204 #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%) #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -0.656204 #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> #> Parameter settings [===============================================================================================] 10/10 (100%) #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -1.424655 #> [LightGBM] [Info] Start training from score -0.656204 #> [LightGBM] [Info] Start training from score -1.422637 #> [LightGBM] [Info] Start training from score -1.428239 #> [LightGBM] [Info] Start training from score -0.655482 head(tuner_results_grid) #> setting_id metric_optim_mean nrounds bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves max_depth #> 1: 1 0.1353093 33 0.6 0.6 4 0.2 18 -1 #> 2: 2 0.1282925 59 0.8 1.0 10 0.2 6 -1 #> 3: 3 0.2360723 100 0.8 0.8 4 0.1 2 -1 #> 4: 4 0.1298904 71 1.0 0.8 4 0.1 10 -1 #> 5: 5 0.1357692 32 1.0 0.6 6 0.2 18 -1 #> 6: 6 0.1313455 64 1.0 1.0 8 0.1 14 -1 #> verbose objective metric num_class #> 1: -1 multiclass multi_logloss 3 #> 2: -1 multiclass multi_logloss 3 #> 3: -1 multiclass multi_logloss 3 #> 4: -1 multiclass multi_logloss 3 #> 5: -1 multiclass multi_logloss 3 #> 6: -1 multiclass multi_logloss 3 ## ----------------------------------------------------------------------------- tuner <- mlexperiments::MLTuneParameters$new( learner = mllrnrs::LearnerLightgbm$new( metric_optimization_higher_better = FALSE ), strategy = "bayesian", ncores = ncores, seed = seed ) tuner$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 bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves gpUtility acqOptimum inBounds #> 1: 0 1 0.6 0.6 4 0.2 18 NA FALSE TRUE #> 2: 0 2 0.8 1.0 10 0.2 6 NA FALSE TRUE #> 3: 0 3 0.8 0.8 4 0.1 2 NA FALSE TRUE #> 4: 0 4 1.0 0.8 4 0.1 10 NA FALSE TRUE #> 5: 0 5 1.0 0.6 6 0.2 18 NA FALSE TRUE #> 6: 0 6 1.0 1.0 8 0.1 14 NA FALSE TRUE #> Elapsed Score metric_optim_mean nrounds errorMessage max_depth verbose objective metric num_class #> 1: 1.283 -0.1353093 0.1353093 33 NA -1 -1 multiclass multi_logloss 3 #> 2: 1.300 -0.1282925 0.1282925 59 NA -1 -1 multiclass multi_logloss 3 #> 3: 1.277 -0.2360723 0.2360723 100 NA -1 -1 multiclass multi_logloss 3 #> 4: 1.460 -0.1298904 0.1298904 71 NA -1 -1 multiclass multi_logloss 3 #> 5: 0.360 -0.1357692 0.1357692 32 NA -1 -1 multiclass multi_logloss 3 #> 6: 0.561 -0.1313455 0.1313455 64 NA -1 -1 multiclass multi_logloss 3 ## ----------------------------------------------------------------------------- validator <- mlexperiments::MLCrossValidation$new( learner = mllrnrs::LearnerLightgbm$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$execute() #> #> CV fold: Fold1 #> #> CV fold: Fold2 #> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%) #> #> CV fold: Fold3 #> CV progress [========================================================================================================] 3/3 (100%) #> head(validator_results) #> fold performance bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves nrounds max_depth verbose #> 1: Fold1 0.9674260 0.8 0.6 8 0.1 14 66 -1 -1 #> 2: Fold2 0.9534347 0.8 0.6 8 0.1 14 66 -1 -1 #> 3: Fold3 0.9549840 0.8 0.6 8 0.1 14 66 -1 -1 #> objective metric num_class #> 1: multiclass multi_logloss 3 #> 2: multiclass multi_logloss 3 #> 3: multiclass multi_logloss 3 ## ----------------------------------------------------------------------------- validator <- mlexperiments::MLNestedCV$new( learner = mllrnrs::LearnerLightgbm$new( metric_optimization_higher_better = FALSE ), strategy = "grid", fold_list = fold_list, k_tuning = 3L, ncores = ncores, seed = seed ) validator$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 #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> #> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%) #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%) #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%) #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%) #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%) #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%) #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%) #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [===============================================================================================] 10/10 (100%) #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> CV fold: Fold2 #> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%) #> [LightGBM] [Info] Start training from score -1.423260 #> [LightGBM] [Info] Start training from score -1.427452 #> [LightGBM] [Info] Start training from score -0.655556 #> [LightGBM] [Info] Start training from score -1.428460 #> [LightGBM] [Info] Start training from score -1.420092 #> [LightGBM] [Info] Start training from score -0.656564 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> [LightGBM] [Info] Start training from score -1.423260 #> [LightGBM] [Info] Start training from score -1.427452 #> [LightGBM] [Info] Start training from score -0.655556 #> [LightGBM] [Info] Start training from score -1.428460 #> [LightGBM] [Info] Start training from score -1.420092 #> [LightGBM] [Info] Start training from score -0.656564 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%) #> [LightGBM] [Info] Start training from score -1.423260 #> [LightGBM] [Info] Start training from score -1.427452 #> [LightGBM] [Info] Start training from score -0.655556 #> [LightGBM] [Info] Start training from score -1.428460 #> [LightGBM] [Info] Start training from score -1.420092 #> [LightGBM] [Info] Start training from score -0.656564 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%) #> [LightGBM] [Info] Start training from score -1.423260 #> [LightGBM] [Info] Start training from score -1.427452 #> [LightGBM] [Info] Start training from score -0.655556 #> [LightGBM] [Info] Start training from score -1.428460 #> [LightGBM] [Info] Start training from score -1.420092 #> [LightGBM] [Info] Start training from score -0.656564 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%) #> [LightGBM] [Info] Start training from score -1.423260 #> [LightGBM] [Info] Start training from score -1.427452 #> [LightGBM] [Info] Start training from score -0.655556 #> [LightGBM] [Info] Start training from score -1.428460 #> [LightGBM] [Info] Start training from score -1.420092 #> [LightGBM] [Info] Start training from score -0.656564 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%) #> [LightGBM] [Info] Start training from score -1.423260 #> [LightGBM] [Info] Start training from score -1.427452 #> [LightGBM] [Info] Start training from score -0.655556 #> [LightGBM] [Info] Start training from score -1.428460 #> [LightGBM] [Info] Start training from score -1.420092 #> [LightGBM] [Info] Start training from score -0.656564 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%) #> [LightGBM] [Info] Start training from score -1.423260 #> [LightGBM] [Info] Start training from score -1.427452 #> [LightGBM] [Info] Start training from score -0.655556 #> [LightGBM] [Info] Start training from score -1.428460 #> [LightGBM] [Info] Start training from score -1.420092 #> [LightGBM] [Info] Start training from score -0.656564 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%) #> [LightGBM] [Info] Start training from score -1.423260 #> [LightGBM] [Info] Start training from score -1.427452 #> [LightGBM] [Info] Start training from score -0.655556 #> [LightGBM] [Info] Start training from score -1.428460 #> [LightGBM] [Info] Start training from score -1.420092 #> [LightGBM] [Info] Start training from score -0.656564 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%) #> [LightGBM] [Info] Start training from score -1.423260 #> [LightGBM] [Info] Start training from score -1.427452 #> [LightGBM] [Info] Start training from score -0.655556 #> [LightGBM] [Info] Start training from score -1.428460 #> [LightGBM] [Info] Start training from score -1.420092 #> [LightGBM] [Info] Start training from score -0.656564 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [===============================================================================================] 10/10 (100%) #> [LightGBM] [Info] Start training from score -1.423260 #> [LightGBM] [Info] Start training from score -1.427452 #> [LightGBM] [Info] Start training from score -0.655556 #> [LightGBM] [Info] Start training from score -1.428460 #> [LightGBM] [Info] Start training from score -1.420092 #> [LightGBM] [Info] Start training from score -0.656564 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> CV fold: Fold3 #> CV progress [========================================================================================================] 3/3 (100%) #> #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%) #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%) #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%) #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%) #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%) #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%) #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%) #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 #> #> Parameter settings [===============================================================================================] 10/10 (100%) #> [LightGBM] [Info] Start training from score -1.421241 #> [LightGBM] [Info] Start training from score -1.429645 #> [LightGBM] [Info] Start training from score -0.655482 #> [LightGBM] [Info] Start training from score -1.424424 #> [LightGBM] [Info] Start training from score -1.428634 #> [LightGBM] [Info] Start training from score -0.654471 #> [LightGBM] [Info] Start training from score -1.422251 #> [LightGBM] [Info] Start training from score -1.426444 #> [LightGBM] [Info] Start training from score -0.656491 head(validator_results) #> fold performance nrounds bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves max_depth verbose #> 1: Fold1 0.9674260 62 0.8 0.6 8 0.1 14 -1 -1 #> 2: Fold2 0.9506435 64 0.8 1.0 10 0.2 6 -1 -1 #> 3: Fold3 0.9559827 100 0.6 0.6 8 0.1 6 -1 -1 #> objective metric num_class #> 1: multiclass multi_logloss 3 #> 2: multiclass multi_logloss 3 ## ----------------------------------------------------------------------------- validator <- mlexperiments::MLNestedCV$new( learner = mllrnrs::LearnerLightgbm$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$execute() #> #> CV fold: Fold1 #> #> Registering parallel backend using 4 cores. #> #> CV fold: Fold2 #> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%) #> #> Registering parallel backend using 4 cores. #> #> CV fold: Fold3 #> CV progress [========================================================================================================] 3/3 (100%) #> #> Registering parallel backend using 4 cores. head(validator_results) #> fold performance bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves nrounds max_depth verbose #> 1: Fold1 0.9727424 0.2000000 0.2559232 2 0.1992638 20 52 -1 -1 #> 2: Fold2 0.9494955 0.8293692 0.5664407 7 0.1203691 11 76 -1 -1 #> 3: Fold3 0.9568462 0.4438889 0.3041453 10 0.1462295 11 67 -1 -1 #> objective metric num_class #> 1: multiclass multi_logloss 3 #> 2: multiclass multi_logloss 3 #> 3: multiclass multi_logloss 3 ## ----------------------------------------------------------------------------- preds_lightgbm <- mlexperiments::predictions( object = validator, newdata = test_x ) ## ----------------------------------------------------------------------------- perf_lightgbm <- mlexperiments::performance( object = validator, prediction_results = preds_lightgbm, y_ground_truth = test_y ) perf_lightgbm #> model performance #> 1: Fold1 0.9596127 #> 2: Fold2 0.9612778 #> 3: Fold3 0.9583793 ## ----------------------------------------------------------------------------- # define the target weights y_weights <- ifelse(train_y == 1, 0.8, ifelse(train_y == 2, 1.2, 1)) head(y_weights) #> [1] 1.2 1.2 0.0 0.8 0.8 0.0 ## ----------------------------------------------------------------------------- tuner_w_weights <- mlexperiments::MLTuneParameters$new( learner = mllrnrs::LearnerLightgbm$new( metric_optimization_higher_better = FALSE ), strategy = "grid", ncores = ncores, seed = seed ) tuner_w_weights$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 nrounds bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves max_depth #> #> 1: 1 0.1294454 29 0.6 0.6 4 0.2 18 -1 #> 2: 2 0.1221349 51 0.8 1.0 10 0.2 6 -1 #> 3: 3 0.2240799 100 0.8 0.8 4 0.1 2 -1 #> 4: 4 0.1194221 75 1.0 0.8 4 0.1 10 -1 #> 5: 5 0.1281037 32 1.0 0.6 6 0.2 18 -1 #> 6: 6 0.1245721 60 1.0 1.0 8 0.1 14 -1 #> verbose objective metric num_class #> #> 1: -1 multiclass multi_logloss 3 #> 2: -1 multiclass multi_logloss 3 #> 3: -1 multiclass multi_logloss 3 #> 4: -1 multiclass multi_logloss 3 #> 5: -1 multiclass multi_logloss 3 #> 6: -1 multiclass multi_logloss 3 ## ----------------------------------------------------------------------------- validator_w_weights <- mlexperiments::MLCrossValidation$new( learner = mllrnrs::LearnerLightgbm$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 nrounds bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves max_depth verbose objective #> #> 1: Fold1 0.9700167 75 1 0.8 4 0.1 10 -1 -1 multiclass #> 2: Fold2 0.9554064 75 1 0.8 4 0.1 10 -1 -1 multiclass #> 3: Fold3 0.9539854 75 1 0.8 4 0.1 10 -1 -1 multiclass #> metric num_class #> #> 1: multi_logloss 3 #> 2: multi_logloss 3 #> 3: multi_logloss 3 ## ----include=FALSE------------------------------------------------------------ # nolint end