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")
embeddings <- nlp_word_embeddings_pretrained(sc, input_cols = c("sentence", "token"), output_col = "embeddings")
ner_tagger <- nlp_ner_dl_pretrained(sc, input_cols = c("sentence", "token", "embeddings"), output_col = "ner_tags")
pos_tagger <- nlp_perceptron_pretrained(sc, input_cols = c("sentence", "token"), output_col = "pos")
dependency_parser <- nlp_dependency_parser_pretrained(sc, input_cols = c("sentence", "pos", "token"), output_col = "dependency")
typed_dependency_parser <- nlp_typed_dependency_parser_pretrained(sc, input_cols = c("dependency", "pos", "token"), output_col = "dependency_type")
graph_extractor <- nlp_graph_extraction(sc, input_cols = c("document", "token", "ner_tags"), output_col = "graph")
pipeline <- ml_pipeline(assembler, sentdetect, tokenizer, embeddings, ner_tagger, pos_tagger, dependency_parser,
typed_dependency_parser, graph_extractor)
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())
assign("text_tbl", text_tbl, envir = parent.frame())
})
teardown({
rm(sc, envir = .GlobalEnv)
rm(pipeline, envir = .GlobalEnv)
rm(test_data, envir = .GlobalEnv)
rm(text_tbl, envir = .GlobalEnv)
})
test_that("graph_finisher param setting", {
test_args <- list(
input_col = "string1",
output_col = "string1",
clean_annotations = FALSE,
include_metadata = TRUE,
output_as_array = TRUE
)
test_param_setting(sc, nlp_graph_finisher, test_args)
})
test_that("nlp_graph_finisher spark_connection", {
test_annotator <- nlp_graph_finisher(sc, input_col = "graph", output_col = "graph_finished")
transformed_data <- ml_transform(test_annotator, test_data)
expect_true("graph_finished" %in% colnames(transformed_data))
expect_true(inherits(test_annotator, "nlp_graph_finisher"))
})
test_that("nlp_graph_finisher ml_pipeline", {
test_annotator <- nlp_graph_finisher(pipeline, input_col = "graph", output_col = "graph_finished")
transformed_data <- ml_fit_and_transform(test_annotator, text_tbl)
expect_true("graph_finished" %in% colnames(transformed_data))
})
test_that("nlp_graph_finisher tbl_spark", {
transformed_data <- nlp_graph_finisher(test_data, input_col = "graph", output_col = "graph_finished")
expect_true("graph_finished" %in% colnames(transformed_data))
})
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