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")
sentdetect <- nlp_sentence_detector(sc, input_cols = c("document"), output_col = "sentence")
tokenizer <- nlp_tokenizer(sc, input_cols = c("sentence"), output_col = "token")
pipeline <- ml_pipeline(assembler, sentdetect, tokenizer)
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({
spark_disconnect(sc)
rm(sc, envir = .GlobalEnv)
rm(pipeline, envir = .GlobalEnv)
rm(test_data, envir = .GlobalEnv)
})
test_that("stop_words_cleaner param setting", {
test_args <- list(
input_cols = c("string1"),
output_col = "string1",
case_sensitive = FALSE,
locale = "en_US",
stop_words = c("string1", "string2")
)
test_param_setting(sc, nlp_stop_words_cleaner, test_args)
})
test_that("nlp_stop_words_cleaner spark_connection", {
test_annotator <- nlp_stop_words_cleaner(sc, input_cols = c("token"), output_col = "cleanTokens", stop_words = c("this", "is", "and"))
transformed_data <- ml_transform(test_annotator, test_data)
expect_true("cleanTokens" %in% colnames(transformed_data))
})
test_that("nlp_stop_words_cleaner ml_pipeline", {
test_annotator <- nlp_stop_words_cleaner(pipeline, input_cols = c("token"), output_col = "cleanTokens", stop_words = c("this", "is", "and"))
transformed_data <- ml_fit_and_transform(test_annotator, test_data)
expect_true("cleanTokens" %in% colnames(transformed_data))
})
test_that("nlp_stop_words_cleaner tbl_spark", {
transformed_data <- nlp_stop_words_cleaner(test_data, input_cols = c("token"), output_col = "cleanTokens", stop_words = c("this", "is", "and"))
expect_true("cleanTokens" %in% colnames(transformed_data))
})
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