tests/testthat/test-classifiers.R

In emeyers/NDTr: The Neural Decoding Toolbox in R

# if one needs to regenrate the test data this function can be used
# source('generate_data_to_test_NDTr.R')



load("example_ZD_train_and_test_set.Rda")
rm(training_set, test_set)



# Helper test functions ----------------------------------------------


test_reasonable_classification_accuracy <- function(cl){
  
  test_that("classification results are reasonably accurate", {
    
    # prediction_results <- get_predictions(cl, normalized_training_set, normalized_test_set)
    prediction_results <- get_predictions(cl, count_training_set, count_test_set)
    
    accuracies <- prediction_results %>%
      dplyr::group_by(test_time) %>%
      dplyr::summarize(mean_accuracy = mean(actual_labels == predicted_labels))
    
    expect_gt(dplyr::filter(accuracies, test_time == "stimulus")$mean_accuracy, .51)
    expect_lt(dplyr::filter(accuracies, test_time == "baseline")$mean_accuracy, .31)
    
  })
  
}



test_shuffle_results_at_chance <- function(cl){
  
  test_that("classification results on shuffled data are around chance", {
    
    #prediction_results <- get_predictions(cl, shuffled_normalized_training_set, shuffled_normalized_test_set)
    prediction_results <- get_predictions(cl, shuffled_count_training_set, shuffled_count_test_set)
    
    accuracies <- prediction_results %>%
      dplyr::group_by(test_time) %>%
      dplyr::summarize(mean_accuracy = mean(actual_labels == predicted_labels))
    
    expect_lt(dplyr::filter(accuracies, test_time == "stimulus")$mean_accuracy, .3)
    expect_lt(dplyr::filter(accuracies, test_time == "baseline")$mean_accuracy, .3)
    
  })
}
  
  



# test cl_max_correlation -----------------------------------------------------

test_that("the cl_max_correlation() classifier is working", {
  
  # test classifier has required methods and returns correctly formatted results
  cl <- cl_max_correlation()
  expect_null(test_valid_classifier(cl))

  test_reasonable_classification_accuracy(cl)
  test_shuffle_results_at_chance(cl)

})





# test cl_poison_naive_bayes --------------------------------------------------

test_that("the cl_poisson_naive_bayes() classifier is working", {
  
  cl <- cl_poisson_naive_bayes()
  expect_null(test_valid_classifier(cl))

  test_reasonable_classification_accuracy(cl)
  test_shuffle_results_at_chance(cl)

})





# test cl_svm -----------------------------------------------------------------

test_that("the cl_svm() claissifier is working", {
  
  cl <- cl_svm()
  expect_null(test_valid_classifier(cl))
  test_reasonable_classification_accuracy(cl)
  test_shuffle_results_at_chance(cl)

  
  cl <- cl_svm(kernel = "linear")
  expect_null(test_valid_classifier(cl))
  test_reasonable_classification_accuracy(cl)
  test_shuffle_results_at_chance(cl)

})