tests/testthat/test-learner_xgboost_tree_S4.R

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

# First test if all selectable learners are also available
familiar:::test_all_learners_available(
  learners = familiar:::.get_available_xgboost_tree_learners(show_general = TRUE))

# Don't perform any further tests on CRAN due to time of running the complete
# test.
testthat::skip_on_cran()
testthat::skip_on_ci()

familiar:::test_all_learners_train_predict_vimp(
  learners = familiar:::.get_available_xgboost_tree_learners(show_general = FALSE),
  hyperparameter_list = list(
    "count" = list(
      "n_boost" = 2,
      "learning_rate" = -1,
      "lambda" = 0.0,
      "alpha" = -6.0,
      "min_child_weight" = 1.04,
      "tree_depth" = 3,
      "sample_size" = 1.0,
      "gamma" = -6.0
    ),
    "continuous" = list(
      "n_boost" = 2,
      "learning_rate" = -1,
      "lambda" = 0.0,
      "alpha" = -6.0,
      "min_child_weight" = 1.04,
      "tree_depth" = 3,
      "sample_size" = 1.0,
      "gamma" = -6.0
    ),
    "binomial" = list(
      "n_boost" = 2,
      "learning_rate" = -1,
      "lambda" = 0.0,
      "alpha" = -6.0,
      "min_child_weight" = 1.04,
      "tree_depth" = 3,
      "sample_size" = 1.0,
      "gamma" = -6.0
    ),
    "multinomial" = list(
      "n_boost" = 2,
      "learning_rate" = -1,
      "lambda" = 0.0,
      "alpha" = -6.0,
      "min_child_weight" = 1.04,
      "tree_depth" = 3,
      "sample_size" = 1.0,
      "gamma" = -6.0
    ),
    "survival" = list(
      "n_boost" = 2,
      "learning_rate" = -1,
      "lambda" = 0.0,
      "alpha" = -6.0,
      "min_child_weight" = 1.04,
      "tree_depth" = 3,
      "sample_size" = 1.0,
      "gamma" = -6.0
    )
  )
)

familiar:::test_all_learners_parallel_train_predict_vimp(
  learners = familiar:::.get_available_xgboost_tree_learners(show_general = FALSE),
  hyperparameter_list = list(
    "count" = list(
      "n_boost" = 2,
      "learning_rate" = -1,
      "lambda" = 0.0,
      "alpha" = -6.0,
      "min_child_weight" = 1.04,
      "tree_depth" = 3,
      "sample_size" = 1.0,
      "gamma" = -6.0
    ),
    "continuous" = list(
      "n_boost" = 2,
      "learning_rate" = -1,
      "lambda" = 0.0,
      "alpha" = -6.0,
      "min_child_weight" = 1.04,
      "tree_depth" = 3,
      "sample_size" = 1.0,
      "gamma" = -6.0
    ),
    "binomial" = list(
      "n_boost" = 2,
      "learning_rate" = -1,
      "lambda" = 0.0,
      "alpha" = -6.0,
      "min_child_weight" = 1.04,
      "tree_depth" = 3,
      "sample_size" = 1.0,
      "gamma" = -6.0
    ),
    "multinomial" = list(
      "n_boost" = 2,
      "learning_rate" = -1,
      "lambda" = 0.0,
      "alpha" = -6.0,
      "min_child_weight" = 1.04,
      "tree_depth" = 3,
      "sample_size" = 1.0,
      "gamma" = -6.0
    ),
    "survival" = list(
      "n_boost" = 2,
      "learning_rate" = -1,
      "lambda" = 0.0,
      "alpha" = -6.0,
      "min_child_weight" = 1.04,
      "tree_depth" = 3,
      "sample_size" = 1.0,
      "gamma" = -6.0
    )
  )
)

# Count outcome tests-----------------------------------------------------------

# Create test data sets.
good_data <- familiar:::test_create_good_data("count")
wide_data <- familiar:::test_create_wide_data("count")

# Train the model using the good dataset.
good_model <- familiar:::test_train(
  data = good_data,
  cluster_method = "none",
  imputation_method = "simple",
  hyperparameter_list = list(
    "sign_size" = familiar:::get_n_features(good_data),
    "n_boost" = 2,
    "learning_rate" = -1,
    "lambda" = 0.0,
    "alpha" = -6.0,
    "min_child_weight" = 1.04,
    "tree_depth" = 3,
    "sample_size" = 1.0,
    "gamma" = -6.0),
  learner = "xgboost_tree_gaussian")

# Train the model using wide data.
wide_model <- familiar:::test_train(
  data = wide_data,
  cluster_method = "none",
  imputation_method = "simple",
  hyperparameter_list = list(
    "sign_size" = familiar:::get_n_features(wide_data),
    "n_boost" = 2,
    "learning_rate" = -1,
    "lambda" = 0.0,
    "alpha" = -6.0,
    "min_child_weight" = 1.04,
    "tree_depth" = 3,
    "sample_size" = 1.0,
    "gamma" = -6.0),
  learner = "xgboost_tree_gaussian")

testthat::test_that("Extreme gradient boosting tree model trained correctly", {
  # Model trained
  testthat::expect_equal(familiar:::model_is_trained(good_model), TRUE)

  # Check that no deprecation warnings are given.
  familiar:::test_not_deprecated(good_model@messages$warning)

  # Test that no errors appear.
  testthat::expect_equal(good_model@messages$error, NULL)
})

testthat::test_that("Extreme gradient boosting tree model has variable importance", {
  # Extract the variable importance table.
  vimp_table <- familiar:::get_vimp_table(good_model)

  # Expect that the vimp table has less than 10 rows.
  testthat::expect_equal(nrow(vimp_table) <= get_n_features(good_data), TRUE)

  # Expect that the names are the same as that of the features.
  testthat::expect_true(all(vimp_table$name %in% familiar:::get_feature_columns(good_data)))

  # Expect that avg_rooms has rank 1 and lower_status_percentage has rank 2.
  testthat::expect_true(vimp_table[rank == 1, ]$name %in% c(
    "avg_rooms", "lower_status_percentage", "per_capita_crime", "residence_before_1940_proportion"))
  testthat::expect_true(vimp_table[rank == 2, ]$name %in% c(
    "avg_rooms", "lower_status_percentage", "per_capita_crime", "residence_before_1940_proportion"))
})

testthat::test_that("Extreme gradient boosting tree model can train on wide data", {
  # Model trained
  testthat::expect_equal(familiar:::model_is_trained(wide_model), TRUE)

  # Variable importance table is absent.
  testthat::expect_true(familiar:::is_empty(familiar:::get_vimp_table(wide_model)))

  # Valid predictions.
  testthat::expect_true(familiar:::any_predictions_valid(
    familiar:::.predict(wide_model, wide_data),
    outcome_type = wide_data@outcome_type))

  # Test that no deprecation warnings are given.
  familiar:::test_not_deprecated(wide_model@messages$warning)

  # Test that no errors appear.
  testthat::expect_equal(wide_model@messages$error, NULL)
})

# Continuous outcome tests------------------------------------------------------

# Create test data sets.
good_data <- familiar:::test_create_good_data("continuous")
wide_data <- familiar:::test_create_wide_data("continuous")

# Train the model using the good dataset.
good_model <- familiar:::test_train(
  data = good_data,
  cluster_method = "none",
  imputation_method = "simple",
  hyperparameter_list = list(
    "sign_size" = familiar:::get_n_features(good_data),
    "n_boost" = 2,
    "learning_rate" = -1,
    "lambda" = 0.0,
    "alpha" = -6.0,
    "min_child_weight" = 1.04,
    "tree_depth" = 3,
    "sample_size" = 1.0,
    "gamma" = -6.0),
  learner = "xgboost_tree_gaussian")

# Train the model using wide data.
wide_model <- familiar:::test_train(
  data = wide_data,
  cluster_method = "none",
  imputation_method = "simple",
  hyperparameter_list = list(
    "sign_size" = familiar:::get_n_features(wide_data),
    "n_boost" = 2,
    "learning_rate" = -1,
    "lambda" = 0.0,
    "alpha" = -6.0,
    "min_child_weight" = 1.04,
    "tree_depth" = 3,
    "sample_size" = 1.0,
    "gamma" = -6.0),
  learner = "xgboost_tree_gaussian")

testthat::test_that("Extreme gradient boosting tree model trained correctly", {
  # Model trained
  testthat::expect_equal(familiar:::model_is_trained(good_model), TRUE)

  # Check that no deprecation warnings are given.
  familiar:::test_not_deprecated(good_model@messages$warning)

  # Test that no errors appear.
  testthat::expect_equal(good_model@messages$error, NULL)
})

testthat::test_that("Extreme gradient boosting tree model has variable importance", {
  # Extract the variable importance table.
  vimp_table <- familiar:::get_vimp_table(good_model)

  # Expect that the vimp table has two rows.
  testthat::expect_equal(nrow(vimp_table), 10)

  # Expect that the names are the same as that of the features.
  testthat::expect_true(all(vimp_table$name %in% familiar:::get_feature_columns(good_data)))

  # Expect that avginc has rank 1 and calwpct has rank 2.
  testthat::expect_equal(vimp_table[rank == 1, ]$name, "avginc")
  testthat::expect_equal(vimp_table[rank == 2, ]$name, "calwpct")
})

testthat::test_that("Extreme gradient boosting tree model can train on wide data", {
  # Model trained
  testthat::expect_equal(familiar:::model_is_trained(wide_model), TRUE)

  # Variable importance table is absent.
  testthat::expect_true(familiar:::is_empty(familiar:::get_vimp_table(wide_model)))

  # Valid predictions.
  testthat::expect_true(familiar:::any_predictions_valid(
    familiar:::.predict(wide_model, wide_data),
    outcome_type = wide_data@outcome_type))

  # Test that no deprecation warnings are given.
  familiar:::test_not_deprecated(wide_model@messages$warning)

  # Test that no errors appear.
  testthat::expect_equal(wide_model@messages$error, NULL)
})

# Binomial tests----------------------------------------------------------------

# Create test data sets.
good_data <- familiar:::test_create_good_data("binomial")
wide_data <- familiar:::test_create_wide_data("binomial")

# Train the model using the good dataset.
good_model <- familiar:::test_train(
  data = good_data,
  cluster_method = "none",
  imputation_method = "simple",
  hyperparameter_list = list(
    "sign_size" = familiar:::get_n_features(good_data),
    "n_boost" = 2,
    "learning_rate" = -1,
    "lambda" = 0.0,
    "alpha" = -6.0,
    "min_child_weight" = 1.04,
    "tree_depth" = 3,
    "sample_size" = 1.0,
    "gamma" = -6.0),
  learner = "xgboost_tree_logistic")

# Train the model using wide data.
wide_model <- familiar:::test_train(
  data = wide_data,
  cluster_method = "none",
  imputation_method = "simple",
  hyperparameter_list = list(
    "sign_size" = familiar:::get_n_features(wide_data),
    "n_boost" = 2,
    "learning_rate" = -1,
    "lambda" = 0.0,
    "alpha" = -6.0,
    "min_child_weight" = 1.04,
    "tree_depth" = 3,
    "sample_size" = 1.0,
    "gamma" = -6.0),
  learner = "xgboost_tree_logistic")

testthat::test_that("Extreme gradient boosting tree model trained correctly", {
  # Model trained
  testthat::expect_equal(familiar:::model_is_trained(good_model), TRUE)

  # Check that no deprecation warnings are given.
  familiar:::test_not_deprecated(good_model@messages$warning)

  # Test that no errors appear.
  testthat::expect_equal(good_model@messages$error, NULL)
})


testthat::test_that("Extreme gradient boosting tree model has variable importance", {
  # Extract the variable importance table.
  vimp_table <- familiar:::get_vimp_table(good_model)

  # Expect that the vimp table has two rows.
  testthat::expect_equal(nrow(vimp_table), 6)

  # Expect that the names are the same as that of the features.
  testthat::expect_true(all(vimp_table$name %in% familiar:::get_feature_columns(good_data)))

  # Expect that cell_shape_uniformity has rank 1 and epithelial_cell_size has rank 2.
  testthat::expect_equal(vimp_table[rank == 1, ]$name, "cell_shape_uniformity")
  testthat::expect_equal(vimp_table[rank == 2, ]$name, "epithelial_cell_size")
})


testthat::test_that("Extreme gradient boosting tree model can train on wide data", {
  # Model trained
  testthat::expect_equal(familiar:::model_is_trained(wide_model), TRUE)

  # Variable importance table is absent
  testthat::expect_true(familiar:::is_empty(familiar:::get_vimp_table(wide_model)))

  # Valid predictions.
  testthat::expect_true(familiar:::any_predictions_valid(
    familiar:::.predict(wide_model, wide_data),
    outcome_type = wide_data@outcome_type))

  # Test that no deprecation warnings are given.
  familiar:::test_not_deprecated(wide_model@messages$warning)

  # Test that no errors appear.
  testthat::expect_equal(wide_model@messages$error, NULL)
})

# Multinomial tests-------------------------------------------------------------

# Create test data sets.
good_data <- familiar:::test_create_good_data("multinomial")
wide_data <- familiar:::test_create_wide_data("multinomial")

# Train the model using the good dataset.
good_model <- familiar:::test_train(
  data = good_data,
  cluster_method = "none",
  imputation_method = "simple",
  hyperparameter_list = list(
    "sign_size" = familiar:::get_n_features(good_data),
    "n_boost" = 2,
    "learning_rate" = -1,
    "lambda" = 0.0,
    "alpha" = -6.0,
    "min_child_weight" = 1.04,
    "tree_depth" = 3,
    "sample_size" = 1.0,
    "gamma" = -6.0),
  learner = "xgboost_tree_logistic")

# Train the model using wide data.
wide_model <- familiar:::test_train(
  data = wide_data,
  cluster_method = "none",
  imputation_method = "simple",
  hyperparameter_list = list(
    "sign_size" = familiar:::get_n_features(wide_data),
    "n_boost" = 2,
    "learning_rate" = -1,
    "lambda" = 0.0,
    "alpha" = -6.0,
    "min_child_weight" = 1.04,
    "tree_depth" = 3,
    "sample_size" = 1.0,
    "gamma" = -6.0),
  learner = "xgboost_tree_logistic")

testthat::test_that("Extreme gradient boosting tree model trained correctly", {
  # Model trained
  testthat::expect_equal(familiar:::model_is_trained(good_model), TRUE)

  # Check that no deprecation warnings are given.
  familiar:::test_not_deprecated(good_model@messages$warning)

  # Test that no errors appear.
  testthat::expect_equal(good_model@messages$error, NULL)
})


testthat::test_that("Extreme gradient boosting tree model has variable importance", {
  # Extract the variable importance table.
  vimp_table <- familiar:::get_vimp_table(good_model)

  # Expect that the vimp table has two rows.
  testthat::expect_equal(nrow(vimp_table), 4)

  # Expect that the names are the same as that of the features.
  testthat::expect_true(all(familiar:::get_feature_columns(good_data) %in% vimp_table$name))

  # Expect that cell_shape_uniformity has rank 1 and bare_nuclei has rank 2.
  testthat::expect_equal(vimp_table[rank == 1, ]$name, "Petal_Length")
  testthat::expect_equal(vimp_table[rank == 2, ]$name, "Petal_Width")
})

testthat::test_that("Extreme gradient boosting tree model can train on wide data", {
  # Model trained
  testthat::expect_equal(familiar:::model_is_trained(wide_model), TRUE)

  # Variable importance table is absent.
  testthat::expect_true(familiar:::is_empty(familiar:::get_vimp_table(wide_model)))

  # Valid predictions.
  testthat::expect_true(familiar:::any_predictions_valid(
    familiar:::.predict(wide_model, wide_data),
    outcome_type = wide_data@outcome_type))

  # Test that no deprecation warnings are given.
  familiar:::test_not_deprecated(wide_model@messages$warning)

  # Test that no errors appear.
  testthat::expect_equal(wide_model@messages$error, NULL)
})

# Survival tests----------------------------------------------------------------

# Create test data sets.
good_data <- familiar:::test_create_good_data("survival")
wide_data <- familiar:::test_create_wide_data("survival")

# Train the model using the good dataset.
good_model <- familiar:::test_train(
  data = good_data,
  cluster_method = "none",
  imputation_method = "simple",
  hyperparameter_list = list(
    "sign_size" = familiar:::get_n_features(good_data),
    "n_boost" = 2,
    "learning_rate" = -1,
    "lambda" = 0.0,
    "alpha" = -6.0,
    "min_child_weight" = 1.04,
    "tree_depth" = 3,
    "sample_size" = 1.0,
    "gamma" = -6.0),
  time_max = 1832,
  learner = "xgboost_tree_cox")

# Train the model using wide data.
wide_model <- familiar:::test_train(
  data = wide_data,
  cluster_method = "none",
  imputation_method = "simple",
  hyperparameter_list = list(
    "sign_size" = familiar:::get_n_features(wide_data),
    "n_boost" = 2,
    "learning_rate" = -1,
    "lambda" = 0.0,
    "alpha" = -6.0,
    "min_child_weight" = 1.04,
    "tree_depth" = 3,
    "sample_size" = 1.0,
    "gamma" = -6.0),
  time_max = 1832,
  learner = "xgboost_tree_cox")

testthat::test_that("Extreme gradient boosting tree model trained correctly", {
  # Model trained
  testthat::expect_equal(familiar:::model_is_trained(good_model), TRUE)

  # Test the prediction type
  testthat::expect_equal(
    familiar:::get_prediction_type(good_model), 
    "hazard_ratio")

  # Test that the model predicts hazard ratios
  testthat::expect_equal(
    familiar:::get_prediction_type(good_model, type = "survival_probability"),
    "survival_probability")

  # Check that no deprecation warnings are given.
  familiar:::test_not_deprecated(good_model@messages$warning)

  # Test that no errors appear.
  testthat::expect_equal(good_model@messages$error, NULL)
})

testthat::test_that("Extreme gradient boosting tree model has variable importance", {
  # Extract the variable importance table.
  vimp_table <- familiar:::get_vimp_table(good_model)

  # Expect that the vimp table has three rows.
  testthat::expect_equal(nrow(vimp_table) %in% c(2, 3), TRUE)

  # Expect that the names are the same as that of the features.
  testthat::expect_true(
    any(familiar:::get_feature_columns(good_data) %in% vimp_table$name))

  # Expect that nodes has rank 1 and rx has rank 2.
  testthat::expect_equal(vimp_table[rank == 1, ]$name, "nodes")
  testthat::expect_equal(vimp_table[rank == 2, ]$name, "rx")
})

testthat::test_that("Extreme gradient boosting tree model can train and predict on wide data", {
  # Model was trained
  testthat::expect_equal(familiar:::model_is_trained(wide_model), TRUE)

  # Variable importance table is absent
  testthat::expect_true(familiar:::is_empty(familiar:::get_vimp_table(wide_model)))

  # Valid predictions are present.
  testthat::expect_true(familiar:::any_predictions_valid(
    familiar:::.predict(wide_model, wide_data),
    outcome_type = wide_data@outcome_type))

  # Valid survival probability predictions can be made.
  testthat::expect_true(familiar:::any_predictions_valid(
    familiar:::.predict(wide_model, wide_data, type = "survival_probability", time = 1000),
    outcome_type = wide_data@outcome_type))

  # Test that no deprecation warnings are given.
  familiar:::test_not_deprecated(wide_model@messages$warning)

  # Test that no errors appear.
  testthat::expect_equal(wide_model@messages$error, NULL)
})

testthat::skip("Skip hyperparameter optimisation, unless manual.")

familiar:::test_hyperparameter_optimisation(
  learners = familiar:::.get_available_xgboost_tree_learners(show_general = TRUE),
  debug = FALSE,
  parallel = FALSE
)