tests/testthat/test-experimental_design.R

In familiar: End-to-End Automated Machine Learning and Model Evaluation

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
)