tests/testthat/test-03_EmbeddedText.R

In aifeducation: Artificial Intelligence for Education

testthat::skip_on_cran()
testthat::skip_if_not(
  condition = check_aif_py_modules(trace = FALSE),
  message = "Necessary python modules not available"
)

# SetUp Test---------------------------------------------------------------------
root_path_general_data<-testthat::test_path("test_data_tmp/Embeddings")
# root_path_data=testthat::test_path("test_data/EmbeddedText")
# if(dir.exists(testthat::test_path("test_artefacts"))==FALSE){
#  dir.create(testthat::test_path("test_artefacts"))
# }
# root_path_results=testthat::test_path("test_artefacts/EmbeddedText")
# if(dir.exists(root_path_results)==FALSE){
#  dir.create(root_path_results)
# }

# SetUp datasets
# Disable tqdm progressbar
transformers$logging$disable_progress_bar()
datasets$disable_progress_bars()

# object is imdb_embeddings
imdb_embeddings=load_from_disk(paste0(root_path_general_data, "/imdb_embeddings"))

# Start test---------------------------------------------------------------------
test_that("EmbeddedText - Create", {
  expect_no_error(EmbeddedText$new())

  new_data_set <- EmbeddedText$new()
  expect_no_error(new_data_set$configure(
    model_name = imdb_embeddings$get_model_info()$model_name,
    model_label = imdb_embeddings$get_model_info()$model_label,
    model_date = imdb_embeddings$get_model_info()$model_date,
    model_method = imdb_embeddings$get_model_info()$model_method,
    model_version = imdb_embeddings$get_model_info()$model_version,
    model_language = imdb_embeddings$get_model_info()$model_language,
    param_seq_length = imdb_embeddings$get_model_info()$param_seq_length,
    param_chunks = imdb_embeddings$get_model_info()$param_chunks,
    param_features = imdb_embeddings$get_model_info()$param_features,
    param_overlap = imdb_embeddings$get_model_info()$param_overlap,
    param_emb_layer_min = imdb_embeddings$get_model_info()$param_emb_layer_min,
    param_emb_layer_max = imdb_embeddings$get_model_info()$param_emb_layer_max,
    param_emb_pool_type = imdb_embeddings$get_model_info()$param_emb_pool_type,
    param_aggregation = imdb_embeddings$get_model_info()$param_aggregation,
    embeddings = imdb_embeddings$embeddings
  ))
})

# Test basic parameters--------------------------------------------------------
test_that("EmbeddedText - No FeatureExtractor", {
  new_embedded_text <- EmbeddedText$new()
  new_embedded_text$configure(
    model_name = imdb_embeddings$get_model_info()$model_name,
    model_label = imdb_embeddings$get_model_info()$model_label,
    model_date = imdb_embeddings$get_model_info()$model_date,
    model_method = imdb_embeddings$get_model_info()$model_method,
    model_version = imdb_embeddings$get_model_info()$model_version,
    model_language = imdb_embeddings$get_model_info()$model_language,
    param_seq_length = imdb_embeddings$get_model_info()$param_seq_length,
    param_chunks = imdb_embeddings$get_model_info()$param_chunks,
    param_features = imdb_embeddings$get_features(),
    param_overlap = imdb_embeddings$get_model_info()$param_overlap,
    param_emb_layer_min = imdb_embeddings$get_model_info()$param_emb_layer_min,
    param_emb_layer_max = imdb_embeddings$get_model_info()$param_emb_layer_max,
    param_emb_pool_type = imdb_embeddings$get_model_info()$param_emb_pool_type,
    param_aggregation = imdb_embeddings$get_model_info()$param_aggregation,
    embeddings = imdb_embeddings$embeddings
  )

  # Correct Features
  expect_equal(new_embedded_text$get_features(), imdb_embeddings$get_features())

  # Correct original features
  expect_equal(new_embedded_text$get_original_features(), imdb_embeddings$get_features())

  # Correct Times
  expect_equal(new_embedded_text$get_times(), imdb_embeddings$get_times())

  # Check model information
  for (entry in names(new_embedded_text$get_model_info())) {
    expect_equal(
      new_embedded_text$get_model_info()[entry],
      imdb_embeddings$get_model_info()[entry]
    )
  }

  # Compression test
  expect_false(new_embedded_text$is_compressed())

  # Conversation
  new_data_set_converted <- new_embedded_text$convert_to_LargeDataSetForTextEmbeddings()
  expect_equal(new_data_set_converted$n_rows(), nrow(imdb_embeddings$embeddings))
  for (entry in names(new_data_set_converted$get_model_info())) {
    expect_equal(
      new_data_set_converted$get_model_info()[entry],
      new_embedded_text$get_model_info()[entry]
    )
  }
})