testthat::skip_on_cran() testthat::skip_on_ci() debug_flag <- FALSE # Simple test familiar:::integrated_test( experimental_design = "fs+mb", fs_method = "none", cluster_method = "none", imputation_method = "simple", parallel = FALSE, skip_evaluation_elements = "all", outcome_type_available = "binomial", debug = debug_flag ) # Bootstrap (without optimisation within bootstraps) familiar:::integrated_test( experimental_design = "bt(fs+mb, 5)", fs_method = "none", cluster_method = "none", imputation_method = "simple", parallel = FALSE, skip_evaluation_elements = "all", outcome_type_available = "binomial", debug = debug_flag ) # Bootstrap (with pre-processing and optimisation within bootstraps) familiar:::integrated_test( experimental_design = "bs(fs+mb, 5)", fs_method = "none", cluster_method = "none", imputation_method = "simple", parallel = FALSE, skip_evaluation_elements = "all", outcome_type_available = "binomial", debug = debug_flag ) # Cross-validation familiar:::integrated_test( experimental_design = "cv(fs+mb, 3)", fs_method = "none", cluster_method = "none", imputation_method = "simple", parallel = FALSE, skip_evaluation_elements = "all", outcome_type_available = "binomial", debug = debug_flag ) # Leave-one-out cross-validation familiar:::integrated_test( experimental_design = "lv(fs+mb)", fs_method = "none", cluster_method = "none", imputation_method = "simple", parallel = FALSE, skip_evaluation_elements = "all", outcome_type_available = "binomial", debug = debug_flag ) # Imbalance corrections using full undersampling familiar:::integrated_test( experimental_design = "ip(fs+mb)", imbalance_correction_method = "full_undersampling", fs_method = "none", cluster_method = "none", imputation_method = "simple", parallel = FALSE, skip_evaluation_elements = "all", outcome_type_available = "binomial", warning_good = "Imbalance partitions are not required as data are not severely imbalanced.", warning_bad = "Imbalance partitions are not required as data are not severely imbalanced.", debug = debug_flag ) # Imbalance corrections using full undersampling familiar:::integrated_test( experimental_design = "ip(fs+mb)", imbalance_correction_method = "random_undersampling", imbalance_n_partitions = 3, fs_method = "none", cluster_method = "none", imputation_method = "simple", parallel = FALSE, skip_evaluation_elements = "all", outcome_type_available = "binomial", warning_good = "Imbalance partitions are not required as data are not severely imbalanced.", warning_bad = "Imbalance partitions are not required as data are not severely imbalanced.", debug = debug_flag )