## ----setup-------------------------------------------------------------------- # nolint start library(mlexperiments) ## ----------------------------------------------------------------------------- library(mlbench) data("BostonHousing") dataset <- BostonHousing |> data.table::as.data.table() |> na.omit() feature_cols <- colnames(dataset)[1:13] target_col <- "medv" cat_vars <- "chas" ## ----------------------------------------------------------------------------- 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 <- data.matrix( dataset[data_split$train, .SD, .SDcols = feature_cols] ) train_y <- dataset[data_split$train, get(target_col)] test_x <- data.matrix( 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(method = "anova") # set arguments for predict function and performance metric, # required for mlexperiments::MLCrossValidation and # mlexperiments::MLNestedCV predict_args <- list(type = "vector") performance_metric <- metric("mse") performance_metric_args <- NULL return_models <- FALSE # required for grid search and initialization of bayesian optimization parameter_grid <- expand.grid( minsplit = seq(2L, 82L, 10L), cp = seq(0.01, 0.1, 0.01), maxdepth = seq(2L, 30L, 5L) ) # 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( minsplit = c(2L, 100L), cp = c(0.01, 0.1), maxdepth = c(2L, 30L) ) optim_args <- list( iters.n = ncores, kappa = 3.5, acq = "ucb" ) ## ----------------------------------------------------------------------------- tuner <- mlexperiments::MLTuneParameters$new( learner = LearnerRpart$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, cat_vars = cat_vars ) tuner_results_grid <- tuner$execute(k = 3) #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%) #> Regression: using 'mean squared error' as optimization metric. #> #> Parameter settings [===============================================================================================] 10/10 (100%) #> Regression: using 'mean squared error' as optimization metric. head(tuner_results_grid) #> setting_id metric_optim_mean minsplit cp maxdepth method #> 1: 1 26.14038 2 0.07 22 anova #> 2: 2 26.14038 32 0.02 27 anova #> 3: 3 26.14038 72 0.10 7 anova #> 4: 4 26.14038 32 0.09 27 anova #> 5: 5 26.14038 52 0.02 12 anova #> 6: 6 26.14038 2 0.04 7 anova ## ----------------------------------------------------------------------------- tuner <- mlexperiments::MLTuneParameters$new( learner = LearnerRpart$new(), strategy = "bayesian", ncores = ncores, seed = seed ) tuner$parameter_grid <- parameter_grid tuner$parameter_bounds <- parameter_bounds tuner$learner_args <- learner_args tuner$optim_args <- optim_args tuner$split_type <- "stratified" tuner$set_data( x = train_x, y = train_y, cat_vars = cat_vars ) tuner_results_bayesian <- tuner$execute(k = 3) #> #> Registering parallel backend using 4 cores. head(tuner_results_bayesian) #> Epoch setting_id minsplit cp maxdepth gpUtility acqOptimum inBounds Elapsed Score metric_optim_mean errorMessage method #> 1: 0 1 2 0.07 22 NA FALSE TRUE 0.049 -26.14038 26.14038 NA anova #> 2: 0 2 32 0.02 27 NA FALSE TRUE 0.049 -26.14038 26.14038 NA anova #> 3: 0 3 72 0.10 7 NA FALSE TRUE 0.049 -26.14038 26.14038 NA anova #> 4: 0 4 32 0.09 27 NA FALSE TRUE 0.049 -26.14038 26.14038 NA anova #> 5: 0 5 52 0.02 12 NA FALSE TRUE 0.027 -26.14038 26.14038 NA anova #> 6: 0 6 2 0.04 7 NA FALSE TRUE 0.027 -26.14038 26.14038 NA anova ## ----------------------------------------------------------------------------- validator <- mlexperiments::MLCrossValidation$new( learner = LearnerRpart$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, cat_vars = cat_vars ) validator_results <- validator$execute() #> #> CV fold: Fold1 #> #> CV fold: Fold2 #> #> CV fold: Fold3 head(validator_results) #> fold performance minsplit cp maxdepth method #> 1: Fold1 29.20022 2 0.07 22 anova #> 2: Fold2 17.76631 2 0.07 22 anova #> 3: Fold3 31.45460 2 0.07 22 anova ## ----------------------------------------------------------------------------- validator <- mlexperiments::MLNestedCV$new( learner = LearnerRpart$new(), strategy = "grid", fold_list = fold_list, k_tuning = 3L, ncores = ncores, seed = seed ) validator$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$set_data( x = train_x, y = train_y, cat_vars = cat_vars ) validator_results <- validator$execute() #> #> CV fold: Fold1 #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%) #> Regression: using 'mean squared error' as optimization metric. #> #> Parameter settings [===============================================================================================] 10/10 (100%) #> Regression: using 'mean squared error' as optimization metric. #> #> CV fold: Fold2 #> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%) #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Parameter settings [===============================================================================================] 10/10 (100%) #> Regression: using 'mean squared error' as optimization metric. #> #> CV fold: Fold3 #> CV progress [========================================================================================================] 3/3 (100%) #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Regression: using 'mean squared error' as optimization metric. #> #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%) #> Regression: using 'mean squared error' as optimization metric. #> #> Parameter settings [===============================================================================================] 10/10 (100%) #> Regression: using 'mean squared error' as optimization metric. head(validator_results) #> fold performance minsplit cp maxdepth method #> 1: Fold1 29.20022 2 0.07 22 anova #> 2: Fold2 17.76631 2 0.07 22 anova #> 3: Fold3 31.45460 2 0.07 22 anova ## ----------------------------------------------------------------------------- validator <- mlexperiments::MLNestedCV$new( learner = LearnerRpart$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$return_models <- TRUE validator$set_data( x = train_x, y = train_y, cat_vars = cat_vars ) validator_results <- validator$execute() #> #> CV fold: Fold1 #> #> Registering parallel backend using 4 cores. #> #> CV fold: Fold2 #> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%) #> #> Registering parallel backend using 4 cores. #> #> CV fold: Fold3 #> CV progress [========================================================================================================] 3/3 (100%) #> #> Registering parallel backend using 4 cores. head(validator_results) #> fold performance minsplit cp maxdepth method #> 1: Fold1 29.20022 2 0.07 22 anova #> 2: Fold2 17.76631 2 0.07 22 anova #> 3: Fold3 31.45460 2 0.07 22 anova ## ----------------------------------------------------------------------------- validator_lm <- mlexperiments::MLCrossValidation$new( learner = LearnerLm$new(), fold_list = fold_list, ncores = ncores, seed = seed ) validator_lm$predict_args <- list(type = "response") validator_lm$performance_metric <- performance_metric validator_lm$performance_metric_args <- performance_metric_args validator_lm$return_models <- TRUE validator_lm$set_data( x = train_x, y = train_y, cat_vars = cat_vars ) validator_lm_results <- validator_lm$execute() #> #> CV fold: Fold1 #> Parameter 'ncores' is ignored for learner 'LearnerLm'. #> #> CV fold: Fold2 #> Parameter 'ncores' is ignored for learner 'LearnerLm'. #> #> CV fold: Fold3 #> Parameter 'ncores' is ignored for learner 'LearnerLm'. head(validator_lm_results) #> fold performance #> 1: Fold1 35.49058 #> 2: Fold2 22.04977 #> 3: Fold3 21.39721 ## ----------------------------------------------------------------------------- mlexperiments::validate_fold_equality( experiments = list(validator, validator_lm) ) #> #> Testing for identical folds in 1 and 2. #> #> Testing for identical folds in 2 and 1. ## ----------------------------------------------------------------------------- preds_rpart <- mlexperiments::predictions( object = validator, newdata = test_x, cat_vars = cat_vars ) preds_lm <- mlexperiments::predictions( object = validator_lm, newdata = test_x, cat_vars = cat_vars ) ## ----------------------------------------------------------------------------- perf_rpart <- mlexperiments::performance( object = validator, prediction_results = preds_rpart, y_ground_truth = test_y, type = "regression" ) perf_lm <- mlexperiments::performance( object = validator_lm, prediction_results = preds_lm, y_ground_truth = test_y, type = "regression" ) ## ----------------------------------------------------------------------------- # combine results for plotting final_results <- rbind( cbind(algorithm = "rpart", perf_rpart), cbind(algorithm = "lm", perf_lm) ) ## ----------------------------------------------------------------------------- # p <- ggpubr::ggdotchart( # data = final_results, # x = "algorithm", # y = "mse", # color = "model", # rotate = TRUE # ) # p ## ----include=FALSE------------------------------------------------------------ # ggplot2::ggsave( # filename = "rpart_chart_reg.png", # plot = p, # width = 7, # height = 5 # ) ## ----include=FALSE------------------------------------------------------------ # nolint end