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

# Create data.table.
data <- familiar:::test.create_good_data_set(outcome_type="binomial",
                                             to_data_object=FALSE)

# Create data assignment object.
experiment_data_assignment <- familiar::precompute_data_assignment(data=data,
                                                                   experimental_design="bs(fs+mb,3)",
                                                                   outcome_type="binomial",
                                                                   outcome_column="cell_malignancy",
                                                                   sample_id_column="id",
                                                                   class_levels=c("benign", "malignant"),
                                                                   verbose=FALSE)

testthat::test_that("Assert that data assignment performs correctly", {
  testthat::expect_equal(is.null(experiment_data_assignment@feature_info), TRUE)
  testthat::expect_equal(is.null(experiment_data_assignment@vimp_table_list), TRUE)
})

# Create feature info object.
experiment_feature_info <- familiar::precompute_feature_info(data=data,
                                                             experiment_data=experiment_data_assignment,
                                                             outcome_type="binomial",
                                                             outcome_column="cell_malignancy",
                                                             sample_id_column="id",
                                                             class_levels=c("benign", "malignant"),
                                                             verbose=FALSE)

testthat::test_that("Assert that feature info computation performs correctly", {
  # Check that 6 subsets are present, namely generic + complete 
  testthat::expect_equal(length(experiment_feature_info@feature_info), 4L)
  
  for(feature_info_set in names(experiment_feature_info@feature_info)){
    for(feature_info in experiment_feature_info@feature_info[[feature_info_set]]){
      # Assert that feature info has the correct class.
      testthat::expect_s4_class(feature_info, "featureInfo")
      
      # Assert that the feature information is complete.
      testthat::expect_equal(familiar:::feature_info_complete(feature_info), feature_info_set != "generic")
    }
  }
  
  # Test contents. 
  testthat::expect_equal(is.null(experiment_feature_info@vimp_table_list), TRUE)
  testthat::expect_equal(experiment_data_assignment@experiment_setup,
                         experiment_feature_info@experiment_setup,
                         ignore_attr=TRUE)
  testthat::expect_equal(experiment_data_assignment@iteration_list,
                         experiment_feature_info@iteration_list,
                         ignore_attr=TRUE)
  testthat::expect_equal(experiment_data_assignment@project_id,
                         experiment_feature_info@project_id)
})



# Create variable importance
experiment_vimp <- familiar::precompute_vimp(data=data,
                                             experiment_data=experiment_feature_info,
                                             fs_method="univariate_regression",
                                             outcome_type="binomial",
                                             outcome_column="cell_malignancy",
                                             sample_id_column="id",
                                             class_levels=c("benign", "malignant"),
                                             verbose=FALSE)

testthat::test_that("Assert that variable importance computation performs correctly", {
  
  # Assert that variable importance data are correct.
  testthat::expect_equal(length(experiment_vimp@vimp_table_list$univariate_regression), 3L)
  for(vimp_table in experiment_vimp@vimp_table_list$univariate_regression){
    testthat::expect_s4_class(vimp_table, "vimpTable")
  }
  
  # Test contents. 
  testthat::expect_equal(experiment_data_assignment@experiment_setup,
                         experiment_vimp@experiment_setup,
                         ignore_attr=TRUE)
  testthat::expect_equal(experiment_data_assignment@iteration_list,
                         experiment_vimp@iteration_list,
                         ignore_attr=TRUE)
  testthat::expect_equal(experiment_feature_info@feature_info,
                         experiment_vimp@feature_info,
                         ignore_attr=TRUE)
  testthat::expect_equal(experiment_data_assignment@project_id,
                         experiment_vimp@project_id)
  
  # Test that get_vimp_table produces three tables.
  vimp_table <- familiar::get_vimp_table(experiment_vimp)
  testthat::expect_equal(length(vimp_table$univariate_regression), 3L)
  
  # Test that aggregate_vimp_table produces one table.
  vimp_table <- familiar::aggregate_vimp_table(experiment_vimp, aggregation_method="borda")
  testthat::expect_s4_class(vimp_table$univariate_regression, "vimpTable")
  vimp_table <- familiar::get_vimp_table(vimp_table)
  testthat::expect_s3_class(vimp_table$univariate_regression, "data.table")
})


# Add additional variable importance methods.
experiment_vimp_3 <- familiar::precompute_vimp(data=data,
                                               experiment_data=experiment_vimp,
                                               fs_method=c("univariate_regression", "mim", "mrmr"),
                                               outcome_type="binomial",
                                               outcome_column="cell_malignancy",
                                               sample_id_column="id",
                                               class_levels=c("benign", "malignant"),
                                               verbose=FALSE)


testthat::test_that("Addition variable importance computation performs correctly", {
  
  # Assert that the vimp tables for the univariate_regression method are not
  # altered.
  for(ii in seq_along(experiment_vimp_3@vimp_table_list$univariate_regression)){
    testthat::expect_equal(experiment_vimp_3@vimp_table_list$univariate_regression[[ii]],
                           experiment_vimp@vimp_table_list$univariate_regression[[ii]],
                           ignore_attr=TRUE)
  }
  
  # Test that 9 variable importance tables were created.
  testthat::expect_equal(length(unlist(familiar::get_vimp_table(experiment_vimp_3), recursive=FALSE)),
                         9L)
  
  # Test that 3 aggregated variable importance tables are created.
  testthat::expect_equal(length(familiar::get_vimp_table(familiar::aggregate_vimp_table(experiment_vimp_3, "borda"))),
                         3L)
  
  testthat::expect_equal(experiment_vimp@project_id,
                         experiment_vimp_3@project_id)
})