#' Spark NLP RENerChunksFilter
#'
#' Spark ML transformer that filters and outputs combinations of relations between
#' extracted entities, for further processing. This annotator is especially useful
#' to create inputs for the RelationExtractionDLModel.
#'
#' See \url{https://nlp.johnsnowlabs.com/docs/en/licensed_annotators#renerchunksfilter}
#'
#' @template roxlate-nlp-algo
#' @template roxlate-inputs-output-params
#' @param max_syntactic_distance Maximal syntactic distance, as threshold (Default: 0)
#' @param relation_pairs List of dash-separated pairs of named entities
#' ("ENTITY1-ENTITY2", e.g. "Biomarker-RelativeDay"), which will be processed
#'
#' @export
nlp_re_ner_chunks_filter <- function(x, input_cols, output_col,
max_syntactic_distance = NULL, relation_pairs,
uid = random_string("re_ner_chunks_filter_")) {
UseMethod("nlp_re_ner_chunks_filter")
}
#' @export
nlp_re_ner_chunks_filter.spark_connection <- function(x, input_cols, output_col,
max_syntactic_distance = NULL, relation_pairs,
uid = random_string("re_ner_chunks_filter_")) {
args <- list(
input_cols = input_cols,
output_col = output_col,
max_syntactic_distance = max_syntactic_distance,
relation_pairs = relation_pairs,
uid = uid
) %>%
validator_nlp_re_ner_chunks_filter()
jobj <- sparklyr::spark_pipeline_stage(
x, "com.johnsnowlabs.nlp.annotators.re.RENerChunksFilter",
input_cols = args[["input_cols"]],
output_col = args[["output_col"]],
uid = args[["uid"]]
) %>%
sparklyr::jobj_set_param("setMaxSyntacticDistance", args[["max_syntactic_distance"]]) %>%
sparklyr::jobj_set_param("setRelationPairs", args[["relation_pairs"]])
new_nlp_re_ner_chunks_filter(jobj)
}
#' @export
nlp_re_ner_chunks_filter.ml_pipeline <- function(x, input_cols, output_col,
max_syntactic_distance = NULL, relation_pairs,
uid = random_string("re_ner_chunks_filter_")) {
stage <- nlp_re_ner_chunks_filter.spark_connection(
x = sparklyr::spark_connection(x),
input_cols = input_cols,
output_col = output_col,
max_syntactic_distance = max_syntactic_distance,
relation_pairs = relation_pairs,
uid = uid
)
sparklyr::ml_add_stage(x, stage)
}
#' @export
nlp_re_ner_chunks_filter.tbl_spark <- function(x, input_cols, output_col,
max_syntactic_distance = NULL, relation_pairs,
uid = random_string("re_ner_chunks_filter_")) {
stage <- nlp_re_ner_chunks_filter.spark_connection(
x = sparklyr::spark_connection(x),
input_cols = input_cols,
output_col = output_col,
max_syntactic_distance = max_syntactic_distance,
relation_pairs = relation_pairs,
uid = uid
)
stage %>% sparklyr::ml_transform(x)
}
#' @import forge
validator_nlp_re_ner_chunks_filter <- function(args) {
args[["input_cols"]] <- cast_string_list(args[["input_cols"]])
args[["output_col"]] <- cast_string(args[["output_col"]])
args[["max_syntactic_distance"]] <- cast_nullable_integer(args[["max_syntactic_distance"]])
args[["relation_pairs"]] <- cast_string_list(args[["relation_pairs"]])
args
}
nlp_float_params.nlp_re_ner_chunks_filter <- function(x) {
return(c())
}
new_nlp_re_ner_chunks_filter <- function(jobj) {
sparklyr::new_ml_transformer(jobj, class = "nlp_re_ner_chunks_filter")
}
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