tests/testthat/test-iucnn_cnn_train.R

In azizka/IUCNN: Neural Networks to Approximate IUCN Red List Conservation Assessments

skip_on_cran()
skip_if_offline()
skip_on_ci()
have_numpy <- reticulate::py_module_available("numpy")
if (isFALSE(have_numpy)) {
  testthat::skip("numpy not available for testing")
}

data("training_occ") #geographic occurrences of species with IUCN assessment
data("training_labels")# the corresponding IUCN assessments

cnn_training_features <- iucnn_cnn_features(training_occ)
cnn_labels <- iucnn_prepare_labels(x = training_labels,
                                   y = cnn_training_features)


test_that("iucnn_cnn_features works", {
  expect_type(cnn_training_features, "list")
  expect_equal(length(cnn_training_features), 889)
})



test_that("multiplication works", {
  trained_model <- iucnn_cnn_train(cnn_training_features,
                                   cnn_labels,
                                   cv_fold = 1,
                                   dropout_rate = 0.1,
                                   overwrite = TRUE)
  expect_equal(length(trained_model), 45)
  expect_s3_class(trained_model, "iucnn_model")
})