--- title: "xgboost: Regression" vignette: > %\VignetteEncoding{UTF-8} %\VignetteIndexEntry{xgboost: Regression} %\VignetteEngine{quarto::html} editor_options: chunk_output_type: console execute: eval: false collapse: true comment: "#>" --- ```{r setup} # nolint start library(mlexperiments) library(mllrnrs) ``` See [https://github.com/kapsner/mllrnrs/blob/main/R/learner_xgboost.R](https://github.com/kapsner/mllrnrs/blob/main/R/learner_xgboost.R) for implementation details. # Preprocessing ## Import and Prepare Data ```{r} library(mlbench) data("BostonHousing") dataset <- BostonHousing |> data.table::as.data.table() |> na.omit() feature_cols <- colnames(dataset)[1:13] target_col <- "medv" ``` ## General Configurations ```{r} 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.xgb.nrounds" = 100L) options("mlexperiments.optim.xgb.early_stopping_rounds" = 10L) ``` ## Generate Training- and Test Data ```{r} 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$train, .SD, .SDcols = feature_cols] ) train_y <- log(dataset[data_split$train, get(target_col)]) test_x <- model.matrix( ~ -1 + ., dataset[data_split$test, .SD, .SDcols = feature_cols] ) test_y <- log(dataset[data_split$test, get(target_col)]) ``` ## Generate Training Data Folds ```{r} fold_list <- splitTools::create_folds( y = train_y, k = 3, type = "stratified", seed = seed ) ``` # Experiments ## Prepare Experiments ```{r} # required learner arguments, not optimized learner_args <- list( objective = "reg:squarederror", eval_metric = "rmse" ) # 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( 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( iters.n = ncores, kappa = 3.5, acq = "ucb" ) ``` ## Hyperparameter Tuning ### Grid Search ```{r} 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$execute(k = 3) #> #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%) #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%) #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%) #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%) #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%) #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%) #> Parameter settings [===============================================================================================] 10/10 (100%) head(tuner_results_grid) #> setting_id metric_optim_mean nrounds subsample colsample_bytree min_child_weight learning_rate max_depth objective #> 1: 1 0.1865926 77 0.6 0.8 5 0.2 1 reg:squarederror #> 2: 2 0.1612372 98 1.0 0.8 5 0.1 5 reg:squarederror #> 3: 3 0.1933602 93 0.8 0.8 5 0.1 1 reg:squarederror #> 4: 4 0.1615993 78 0.6 0.8 5 0.2 5 reg:squarederror #> 5: 5 0.1648096 99 1.0 0.8 1 0.1 5 reg:squarederror #> 6: 6 0.1573879 100 0.8 0.8 5 0.1 5 reg:squarederror #> eval_metric #> 1: rmse #> 2: rmse #> 3: rmse #> 4: rmse #> 5: rmse #> 6: rmse ``` ### Bayesian Optimization ```{r} tuner <- mlexperiments::MLTuneParameters$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 1.569 #> 2: 0 2 1.0 0.8 5 0.1 5 NA FALSE TRUE 1.663 #> 3: 0 3 0.8 0.8 5 0.1 1 NA FALSE TRUE 1.611 #> 4: 0 4 0.6 0.8 5 0.2 5 NA FALSE TRUE 1.611 #> 5: 0 5 1.0 0.8 1 0.1 5 NA FALSE TRUE 0.941 #> 6: 0 6 0.8 0.8 5 0.1 5 NA FALSE TRUE 0.906 #> Score metric_optim_mean nrounds errorMessage objective eval_metric #> 1: -0.1865024 0.1865024 56 NA reg:squarederror rmse #> 2: -0.1607242 0.1607242 89 NA reg:squarederror rmse #> 3: -0.1913163 0.1913163 100 NA reg:squarederror rmse #> 4: -0.1609879 0.1609879 66 NA reg:squarederror rmse #> 5: -0.1573682 0.1573682 100 NA reg:squarederror rmse #> 6: -0.1635603 0.1635603 92 NA reg:squarederror rmse ``` ## k-Fold Cross Validation ```{r} 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$execute() #> #> CV fold: Fold1 #> #> CV fold: Fold2 #> #> CV fold: Fold3 head(validator_results) #> fold performance subsample colsample_bytree min_child_weight learning_rate max_depth nrounds objective eval_metric #> 1: Fold1 0.04193925 0.6 1 1 0.1 5 92 reg:squarederror rmse #> 2: Fold2 0.05079392 0.6 1 1 0.1 5 92 reg:squarederror rmse #> 3: Fold3 0.03915493 0.6 1 1 0.1 5 92 reg:squarederror rmse ``` ## Nested Cross Validation ### Inner Grid Search ```{r} validator <- mlexperiments::MLNestedCV$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 [=====================================>----------------------------------------------------------] 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%) #> CV fold: Fold2 #> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%) #> #> 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%) #> 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 0.04291802 64 0.8 0.8 5 0.1 5 reg:squarederror rmse #> 2: Fold2 0.05138479 76 0.6 1.0 1 0.1 5 reg:squarederror rmse #> 3: Fold3 0.03818053 36 0.6 0.8 5 0.2 5 reg:squarederror rmse ``` ### Inner Bayesian Optimization ```{r} 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 0.04147964 0.6225939 0.9208933 5 0.1326066 5 59 reg:squarederror rmse #> 2: Fold2 0.05881907 1.0000000 0.8000000 1 0.1000000 5 94 reg:squarederror rmse #> 3: Fold3 0.03890190 0.6000000 1.0000000 5 0.2000000 5 37 reg:squarederror rmse ``` ## Holdout Test Dataset Performance ### Predict Outcome in Holdout Test Dataset ```{r} preds_xgboost <- mlexperiments::predictions( object = validator, newdata = test_x ) ``` ### Evaluate Performance on Holdout Test Dataset ```{r} perf_xgboost <- mlexperiments::performance( object = validator, prediction_results = preds_xgboost, y_ground_truth = test_y, type = "regression" ) perf_xgboost #> model performance mse msle mae mape rmse rmsle rsq sse #> 1: Fold1 0.04322328 0.02725729 0.001868252 0.1188479 0.04074989 0.1650978 0.04322328 0.8227146 4.224880 #> 2: Fold2 0.04730978 0.03081692 0.002238216 0.1235033 0.04247960 0.1755475 0.04730978 0.7995622 4.776623 #> 3: Fold3 0.03977942 0.02204549 0.001582402 0.1090010 0.03781531 0.1484773 0.03977942 0.8566129 3.417052 ``` ```{r include=FALSE} # nolint end ```