#' Spark NLP DateMatcher
#'
#' Spark ML transformer that reads from different forms of date and time expressions and converts them to a provided
#' date format. Extracts only ONE date per sentence. Use with sentence detector for more matches.
#' See \url{https://nlp.johnsnowlabs.com/docs/en/annotators#datematcher}
#'
#' @template roxlate-nlp-algo
#' @template roxlate-inputs-output-params
#' @param format Output format of parsed date. Defaults to yyyy/MM/dd
#' @param anchor_date_day Add an anchor day for the relative dates such as a day after tomorrow (Default: -1).
#' By default it will use the current day. The first day of the month has value 1.
#' @param anchor_date_month Add an anchor month for the relative dates such as a day after tomorrow (Default: -1).
#' By default it will use the current month. Month values start from 1, so 1 stands for January.
#' @param anchor_date_year Add an anchor year for the relative dates such as a day after tomorrow (Default: -1).
#' If it is not set, the by default it will use the current year. Example: 2021
#' @param default_day_when_missing Which day to set when it is missing from parsed input (Default: 1)
#' @param read_month_first Whether to interpret dates as MM/DD/YYYY instead of DD/MM/YYYY (Default: true)
#' @param source_language Source language for explicit translation
#'
#' @export
nlp_date_matcher <- function(x, input_cols, output_col,
anchor_date_day = NULL, anchor_date_month = NULL, anchor_date_year = NULL,
default_day_when_missing = NULL, read_month_first = NULL, format = NULL,
source_language = NULL,
uid = random_string("date_matcher_")) {
UseMethod("nlp_date_matcher")
}
#' @export
nlp_date_matcher.spark_connection <- function(x, input_cols, output_col,
anchor_date_day = NULL, anchor_date_month = NULL, anchor_date_year = NULL,
default_day_when_missing = NULL, read_month_first = NULL, format = NULL,
source_language = NULL,
uid = random_string("date_matcher_")) {
args <- list(
input_cols = input_cols,
output_col = output_col,
anchor_date_day = anchor_date_day,
anchor_date_month = anchor_date_month,
anchor_date_year = anchor_date_year,
default_day_when_missing = default_day_when_missing,
read_month_first = read_month_first,
format = format,
source_language = source_language,
uid = uid
) %>%
validator_nlp_date_matcher()
jobj <- sparklyr::spark_pipeline_stage(
x, "com.johnsnowlabs.nlp.annotators.DateMatcher",
input_cols = args[["input_cols"]],
output_col = args[["output_col"]],
uid = args[["uid"]]
) %>%
sparklyr::jobj_set_param("setFormat", args[["format"]]) %>%
sparklyr::jobj_set_param("setAnchorDateDay", args[["anchor_date_day"]]) %>%
sparklyr::jobj_set_param("setAnchorDateMonth", args[["anchor_date_month"]]) %>%
sparklyr::jobj_set_param("setAnchorDateYear", args[["anchor_date_year"]]) %>%
sparklyr::jobj_set_param("setDefaultDayWhenMissing", args[["default_day_when_missing"]]) %>%
sparklyr::jobj_set_param("setReadMonthFirst", args[["read_month_first"]]) %>%
sparklyr::jobj_set_param("setSourceLanguage", args[["source_language"]])
new_nlp_date_matcher(jobj)
}
#' @export
nlp_date_matcher.ml_pipeline <- function(x, input_cols, output_col,
anchor_date_day = NULL, anchor_date_month = NULL, anchor_date_year = NULL,
default_day_when_missing = NULL, read_month_first = NULL, format = NULL,
source_language = NULL,
uid = random_string("date_matcher_")) {
stage <- nlp_date_matcher.spark_connection(
x = sparklyr::spark_connection(x),
input_cols = input_cols,
output_col = output_col,
anchor_date_day = anchor_date_day,
anchor_date_month = anchor_date_month,
anchor_date_year = anchor_date_year,
default_day_when_missing = default_day_when_missing,
read_month_first = read_month_first,
format = format,
source_language = source_language,
uid = uid
)
sparklyr::ml_add_stage(x, stage)
}
#' @export
nlp_date_matcher.tbl_spark <- function(x, input_cols, output_col,
anchor_date_day = NULL, anchor_date_month = NULL, anchor_date_year = NULL,
default_day_when_missing = NULL, read_month_first = NULL, format = NULL,
source_language = NULL,
uid = random_string("date_matcher_")) {
stage <- nlp_date_matcher.spark_connection(
x = sparklyr::spark_connection(x),
input_cols = input_cols,
output_col = output_col,
anchor_date_day = anchor_date_day,
anchor_date_month = anchor_date_month,
anchor_date_year = anchor_date_year,
default_day_when_missing = default_day_when_missing,
read_month_first = read_month_first,
format = format,
source_language = source_language,
uid = uid
)
stage %>% sparklyr::ml_transform(x)
}
#' @import forge
validator_nlp_date_matcher <- function(args) {
args[["input_cols"]] <- cast_string_list(args[["input_cols"]])
args[["output_col"]] <- cast_string(args[["output_col"]])
args[["anchor_date_day"]] <- cast_nullable_integer(args[["anchor_date_day"]])
args[["anchor_date_month"]] <- cast_nullable_integer(args[["anchor_date_month"]])
args[["anchor_date_year"]] <- cast_nullable_integer(args[["anchor_date_year"]])
args[["default_day_when_missing"]] <- cast_nullable_integer(args[["default_day_when_missing"]])
args[["read_month_first"]] <- cast_nullable_logical(args[["read_month_first"]])
args[["format"]] <- cast_nullable_string(args[["format"]])
args[["source_language"]] <- cast_nullable_string(args[["source_language"]])
args
}
new_nlp_date_matcher <- function(jobj) {
sparklyr::new_ml_transformer(jobj, class = "nlp_date_matcher")
}
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