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 = "tokens")
word_embeddings <- nlp_word_embeddings_pretrained(sc, input_cols = c("sentence", "tokens"),
output_col = "embeddings", name = "embeddings_clinical",
remote_loc = "clinical/models")
pos_tagger <- nlp_perceptron_pretrained(sc, input_cols = c("sentence", "tokens"), output_col = "pos_tags",
name = "pos_clinical", remote_loc = "clinical/models")
dependency_parser <- nlp_dependency_parser_pretrained(sc, input_cols = c("sentence", "pos_tags", "tokens"),
output_col = "dependencies",
name = "dependency_conllu")
ner_tagger <- nlp_medical_ner_pretrained(sc, input_cols = c("sentence", "tokens", "embeddings"),
output_col = "ner_tags", name = "jsl_ner_wip_greedy_clinical",
remote_loc = "clinical/models")
ner_chunker <- nlp_ner_converter(sc, input_cols = c("sentence", "tokens", "ner_tags"),
output_col = "ner_chunks")
pipeline <- ml_pipeline(assembler, sentdetect, tokenizer, word_embeddings, pos_tagger,
dependency_parser, ner_tagger, ner_chunker)
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("re_ner_chunks_filter param setting", {
test_args <- list(
input_cols = c("string1", "string2"),
output_col = "string1",
max_syntactic_distance = 4,
relation_pairs = c("string1-string2")
)
test_param_setting(sc, nlp_re_ner_chunks_filter, test_args)
})
test_that("nlp_re_ner_chunks_filter spark_connection", {
relpairs <- c("direction-external_body_part_or_region",
"external_body_part_or_region-direction",
"direction-internal_organ_or_component",
"internal_organ_or_component-direction")
test_annotator <- nlp_re_ner_chunks_filter(sc, input_cols = c("ner_chunks","dependencies"), output_col = "re_ner_chunks",
relation_pairs = relpairs, max_syntactic_distance = 4)
transformed_data <- ml_transform(test_annotator, test_data)
expect_true("re_ner_chunks" %in% colnames(transformed_data))
expect_true(inherits(test_annotator, "nlp_re_ner_chunks_filter"))
})
test_that("nlp_re_ner_chunks_filter ml_pipeline", {
relpairs <- c("direction-external_body_part_or_region",
"external_body_part_or_region-direction",
"direction-internal_organ_or_component",
"internal_organ_or_component-direction")
test_annotator <- nlp_re_ner_chunks_filter(pipeline, input_cols = c("ner_chunks","dependencies"), output_col = "re_ner_chunks",
relation_pairs = relpairs, max_syntactic_distance = 4)
fit_data <- ml_fit(test_annotator, test_data)
transformed_data <- ml_fit_and_transform(test_annotator, text_tbl)
expect_true("re_ner_chunks" %in% colnames(transformed_data))
})
test_that("nlp_re_ner_chunks_filter tbl_spark", {
relpairs <- c("direction-external_body_part_or_region",
"external_body_part_or_region-direction",
"direction-internal_organ_or_component",
"internal_organ_or_component-direction")
transformed_data <- nlp_re_ner_chunks_filter(test_data, input_cols = c("ner_chunks","dependencies"), output_col = "re_ner_chunks",
relation_pairs = relpairs, max_syntactic_distance = 4)
expect_true("re_ner_chunks" %in% colnames(transformed_data))
})
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.