tests/testthat/testthat-document_normalizer.R

setup({
  sc <- testthat_spark_connection()
  text_tbl <- testthat_tbl("test_text")

  # These lines should set a pipeline that will ultimately create the columns needed for testing the annotator
  assembler <- nlp_document_assembler(sc, input_col = "text", output_col = "document")

  pipeline <- ml_pipeline(assembler)
  test_data <- ml_fit_and_transform(pipeline, text_tbl)

  assign("sc", sc, envir = parent.frame())
  assign("pipeline", pipeline, envir = parent.frame())
  assign("test_data", test_data, envir = parent.frame())
})

teardown({
  rm(sc, envir = .GlobalEnv)
  rm(pipeline, envir = .GlobalEnv)
  rm(test_data, envir = .GlobalEnv)
})

test_that("document_normalizer param setting", {
  test_args <- list(
    input_cols = c("string1"),
    output_col = "string1",
    action = "string1",
    encoding = "string1",
    lower_case = TRUE,
    patterns = c("string1", "string2"),
    policy = "string1",
    replacement = "string1"
  )

  test_param_setting(sc, nlp_document_normalizer, test_args)
})

test_that("nlp_document_normalizer spark_connection", {
  test_annotator <- nlp_document_normalizer(sc, input_cols = c("document"), output_col = "normalizedDocument",
                                            patterns = c("<[^>]*>"))
  transformed_data <- ml_transform(test_annotator, test_data)
  expect_true("normalizedDocument" %in% colnames(transformed_data))
  expect_true(inherits(test_annotator, "nlp_document_normalizer"))
})

test_that("nlp_document_normalizer ml_pipeline", {
  test_annotator <- nlp_document_normalizer(pipeline, input_cols = c("document"), output_col = "normalizedDocument")
  transformed_data <- ml_fit_and_transform(test_annotator, test_data)
  expect_true("normalizedDocument" %in% colnames(transformed_data))
})

test_that("nlp_document_normalizer tbl_spark", {
  transformed_data <- nlp_document_normalizer(test_data, input_cols = c("document"), output_col = "normalizedDocument")
  expect_true("normalizedDocument" %in% colnames(transformed_data))
})
r-spark/sparknlp documentation built on Oct. 15, 2022, 10:50 a.m.