## ----setup-------------------------------------------------------------------- # nolint start library(mlexperiments) library(mllrnrs) ## ----------------------------------------------------------------------------- library(mlbench) data("BostonHousing") dataset <- BostonHousing |> data.table::as.data.table() |> na.omit() feature_cols <- colnames(dataset)[1:13] target_col <- "medv" ## ----------------------------------------------------------------------------- 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$train, .SD, .SDcols = feature_cols] ) train_y <- dataset[data_split$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( max_depth = -1L, verbose = -1L, objective = "regression", metric = "l2" ) # set arguments for predict function and performance metric, # required for mlexperiments::MLCrossValidation and # mlexperiments::MLNestedCV predict_args <- NULL performance_metric <- metric("rmsle") 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 22.450000 #> [LightGBM] [Info] Start training from score 22.655319 #> [LightGBM] [Info] Start training from score 22.592704 #> [LightGBM] [Info] Start training from score 22.450000 #> [LightGBM] [Info] Start training from score 22.655319 #> [LightGBM] [Info] Start training from score 22.592704 #> [LightGBM] [Info] Start training from score 22.450000 #> [LightGBM] [Info] Start training from score 22.655319 #> [LightGBM] [Info] Start training from score 22.592704 #> [LightGBM] [Info] Start training from score 22.450000 #> [LightGBM] [Info] Start training from score 22.655319 #> [LightGBM] [Info] Start training from score 22.592704 #> #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%) #> [LightGBM] [Info] Start training from score 22.450000 #> [LightGBM] [Info] Start training from score 22.655319 #> [LightGBM] [Info] Start training from score 22.592704 #> #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%) #> [LightGBM] [Info] Start training from score 22.450000 #> [LightGBM] [Info] Start training from score 22.655319 #> [LightGBM] [Info] Start training from score 22.592704 #> #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%) #> [LightGBM] [Info] Start training from score 22.450000 #> [LightGBM] [Info] Start training from score 22.655319 #> [LightGBM] [Info] Start training from score 22.592704 #> #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%) #> [LightGBM] [Info] Start training from score 22.450000 #> [LightGBM] [Info] Start training from score 22.655319 #> [LightGBM] [Info] Start training from score 22.592704 #> #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%) #> [LightGBM] [Info] Start training from score 22.450000 #> [LightGBM] [Info] Start training from score 22.655319 #> [LightGBM] [Info] Start training from score 22.592704 #> #> Parameter settings [===============================================================================================] 10/10 (100%) #> [LightGBM] [Info] Start training from score 22.450000 #> [LightGBM] [Info] Start training from score 22.655319 #> [LightGBM] [Info] Start training from score 22.592704 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 13.59085 85 0.6 0.6 4 0.2 18 -1 #> 2: 2 13.75483 55 0.8 1.0 10 0.2 6 -1 #> 3: 3 21.08526 58 0.8 0.8 4 0.1 2 -1 #> 4: 4 13.31343 92 1.0 0.8 4 0.1 10 -1 #> 5: 5 13.86649 80 1.0 0.6 6 0.2 18 -1 #> 6: 6 14.58646 100 1.0 1.0 8 0.1 14 -1 #> verbose objective metric #> 1: -1 regression l2 #> 2: -1 regression l2 #> 3: -1 regression l2 #> 4: -1 regression l2 #> 5: -1 regression l2 #> 6: -1 regression l2 ## ----------------------------------------------------------------------------- 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 #> 1: 1.081 -13.59085 13.59085 85 NA -1 -1 regression l2 #> 2: 1.072 -13.75483 13.75483 55 NA -1 -1 regression l2 #> 3: 1.044 -21.08526 21.08526 58 NA -1 -1 regression l2 #> 4: 1.126 -13.31343 13.31343 92 NA -1 -1 regression l2 #> 5: 0.104 -13.86649 13.86649 80 NA -1 -1 regression l2 #> 6: 0.106 -14.58646 14.58646 100 NA -1 -1 regression l2 ## ----------------------------------------------------------------------------- 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$execute() #> #> CV fold: Fold1 #> #> CV fold: Fold2 #> #> CV fold: Fold3 head(validator_results) #> fold performance bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves nrounds max_depth verbose #> 1: Fold1 0.1572748 0.6 0.8 2 0.2 10 34 -1 -1 #> 2: Fold2 0.1770563 0.6 0.8 2 0.2 10 34 -1 -1 #> 3: Fold3 0.1439331 0.6 0.8 2 0.2 10 34 -1 -1 #> objective metric #> 1: regression l2 #> 2: regression l2 #> 3: regression l2 ## ----------------------------------------------------------------------------- validator <- mlexperiments::MLNestedCV$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 22.387821 #> [LightGBM] [Info] Start training from score 22.485257 #> [LightGBM] [Info] Start training from score 22.476923 #> [LightGBM] [Info] Start training from score 22.387821 #> [LightGBM] [Info] Start training from score 22.485257 #> [LightGBM] [Info] Start training from score 22.476923 #> [LightGBM] [Info] Start training from score 22.387821 #> [LightGBM] [Info] Start training from score 22.485257 #> [LightGBM] [Info] Start training from score 22.476923 #> [LightGBM] [Info] Start training from score 22.387821 #> [LightGBM] [Info] Start training from score 22.485257 #> [LightGBM] [Info] Start training from score 22.476923 #> [LightGBM] [Info] Start training from score 22.387821 #> [LightGBM] [Info] Start training from score 22.485257 #> [LightGBM] [Info] Start training from score 22.476923 #> #> #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%) #> [LightGBM] [Info] Start training from score 22.387821 #> [LightGBM] [Info] Start training from score 22.485257 #> [LightGBM] [Info] Start training from score 22.476923 #> #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%) #> [LightGBM] [Info] Start training from score 22.387821 #> [LightGBM] [Info] Start training from score 22.485257 #> [LightGBM] [Info] Start training from score 22.476923 #> #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%) #> [LightGBM] [Info] Start training from score 22.387821 #> [LightGBM] [Info] Start training from score 22.485257 #> [LightGBM] [Info] Start training from score 22.476923 #> #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%) #> [LightGBM] [Info] Start training from score 22.387821 #> [LightGBM] [Info] Start training from score 22.485257 #> [LightGBM] [Info] Start training from score 22.476923 #> #> Parameter settings [===============================================================================================] 10/10 (100%) #> [LightGBM] [Info] Start training from score 22.387821 #> [LightGBM] [Info] Start training from score 22.485257 #> [LightGBM] [Info] Start training from score 22.476923 #> #> CV fold: Fold2 #> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%) #> [LightGBM] [Info] Start training from score 22.517722 #> [LightGBM] [Info] Start training from score 22.641401 #> [LightGBM] [Info] Start training from score 22.809677 #> [LightGBM] [Info] Start training from score 22.517722 #> [LightGBM] [Info] Start training from score 22.641401 #> [LightGBM] [Info] Start training from score 22.809677 #> [LightGBM] [Info] Start training from score 22.517722 #> [LightGBM] [Info] Start training from score 22.641401 #> [LightGBM] [Info] Start training from score 22.809677 #> #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%) #> [LightGBM] [Info] Start training from score 22.517722 #> [LightGBM] [Info] Start training from score 22.641401 #> [LightGBM] [Info] Start training from score 22.809677 #> #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%) #> [LightGBM] [Info] Start training from score 22.517722 #> [LightGBM] [Info] Start training from score 22.641401 #> [LightGBM] [Info] Start training from score 22.809677 #> #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%) #> [LightGBM] [Info] Start training from score 22.517722 #> [LightGBM] [Info] Start training from score 22.641401 #> [LightGBM] [Info] Start training from score 22.809677 #> #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%) #> [LightGBM] [Info] Start training from score 22.517722 #> [LightGBM] [Info] Start training from score 22.641401 #> [LightGBM] [Info] Start training from score 22.809677 #> #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%) #> [LightGBM] [Info] Start training from score 22.517722 #> [LightGBM] [Info] Start training from score 22.641401 #> [LightGBM] [Info] Start training from score 22.809677 #> #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%) #> [LightGBM] [Info] Start training from score 22.517722 #> [LightGBM] [Info] Start training from score 22.641401 #> [LightGBM] [Info] Start training from score 22.809677 #> #> Parameter settings [===============================================================================================] 10/10 (100%) #> [LightGBM] [Info] Start training from score 22.517722 #> [LightGBM] [Info] Start training from score 22.641401 #> [LightGBM] [Info] Start training from score 22.809677 #> #> CV fold: Fold3 #> CV progress [========================================================================================================] 3/3 (100%) #> #> [LightGBM] [Info] Start training from score 22.496129 #> [LightGBM] [Info] Start training from score 22.728387 #> [LightGBM] [Info] Start training from score 22.553846 #> [LightGBM] [Info] Start training from score 22.496129 #> [LightGBM] [Info] Start training from score 22.728387 #> [LightGBM] [Info] Start training from score 22.553846 #> [LightGBM] [Info] Start training from score 22.496129 #> [LightGBM] [Info] Start training from score 22.728387 #> [LightGBM] [Info] Start training from score 22.553846 #> [LightGBM] [Info] Start training from score 22.496129 #> [LightGBM] [Info] Start training from score 22.728387 #> [LightGBM] [Info] Start training from score 22.553846 #> [LightGBM] [Info] Start training from score 22.496129 #> [LightGBM] [Info] Start training from score 22.728387 #> [LightGBM] [Info] Start training from score 22.553846 #> [LightGBM] [Info] Start training from score 22.496129 #> [LightGBM] [Info] Start training from score 22.728387 #> [LightGBM] [Info] Start training from score 22.553846 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%) #> [LightGBM] [Info] Start training from score 22.496129 #> [LightGBM] [Info] Start training from score 22.728387 #> [LightGBM] [Info] Start training from score 22.553846 #> #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%) #> [LightGBM] [Info] Start training from score 22.496129 #> [LightGBM] [Info] Start training from score 22.728387 #> [LightGBM] [Info] Start training from score 22.553846 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%) #> [LightGBM] [Info] Start training from score 22.496129 #> [LightGBM] [Info] Start training from score 22.728387 #> [LightGBM] [Info] Start training from score 22.553846 #> #> Parameter settings [===============================================================================================] 10/10 (100%) #> [LightGBM] [Info] Start training from score 22.496129 #> [LightGBM] [Info] Start training from score 22.728387 #> [LightGBM] [Info] Start training from score 22.553846 head(validator_results) #> fold performance nrounds bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves max_depth verbose #> 1: Fold1 0.1856914 99 0.8 0.8 4 0.1 2 -1 -1 #> 2: Fold2 0.1842789 37 0.8 0.6 8 0.1 14 -1 -1 #> 3: Fold3 0.1516625 17 0.6 0.6 4 0.2 18 -1 -1 #> objective metric #> 1: regression l2 #> 2: regression l2 #> 3: regression l2 ## ----------------------------------------------------------------------------- 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 0.1972673 1.0000000 0.2000000 7 0.1000000 2 100 -1 -1 #> 2: Fold2 0.1934754 0.5029800 0.4977050 7 0.1195995 4 52 -1 -1 #> 3: Fold3 0.1391255 0.8050493 0.5902201 2 0.1458152 20 44 -1 -1 #> objective metric #> 1: regression l2 #> 2: regression l2 #> 3: regression l2 ## ----------------------------------------------------------------------------- preds_lightgbm <- mlexperiments::predictions( object = validator, newdata = test_x ) ## ----------------------------------------------------------------------------- perf_lightgbm <- mlexperiments::performance( object = validator, prediction_results = preds_lightgbm, y_ground_truth = test_y, type = "regression" ) perf_lightgbm #> model performance mse msle mae mape rmse rmsle rsq sse #> 1: Fold1 0.1593611 16.258140 0.02539596 2.700096 0.1230206 4.032138 0.1593611 0.7945966 2520.012 #> 2: Fold2 0.1720557 12.614833 0.02960318 2.629003 0.1329465 3.551737 0.1720557 0.8406257 1955.299 #> 3: Fold3 0.1424064 9.666178 0.02027958 2.226562 0.1101779 3.109048 0.1424064 0.8778787 1498.258 ## ----include=FALSE------------------------------------------------------------ # nolint end