#' Feature Transformation -- RegexTokenizer (Transformer)
#'
#' A regex based tokenizer that extracts tokens either by using the provided
#' regex pattern to split the text (default) or repeatedly matching the regex
#' (if \code{gaps} is false). Optional parameters also allow filtering tokens using a
#' minimal length. It returns an array of strings that can be empty.
#'
#' @template roxlate-ml-feature-input-output-col
#' @template roxlate-ml-feature-transformer
#'
#' @param gaps Indicates whether regex splits on gaps (TRUE) or matches tokens (FALSE).
#' @param min_token_length Minimum token length, greater than or equal to 0.
#' @param pattern The regular expression pattern to be used.
#' @param to_lower_case Indicates whether to convert all characters to lowercase before tokenizing.
#'
#' @export
ft_regex_tokenizer <- function(x, input_col = NULL, output_col = NULL, gaps = TRUE,
min_token_length = 1, pattern = "\\s+", to_lower_case = TRUE,
uid = random_string("regex_tokenizer_"), ...) {
check_dots_used()
UseMethod("ft_regex_tokenizer")
}
ml_regex_tokenizer <- ft_regex_tokenizer
#' @export
ft_regex_tokenizer.spark_connection <- function(x, input_col = NULL, output_col = NULL, gaps = TRUE,
min_token_length = 1, pattern = "\\s+", to_lower_case = TRUE,
uid = random_string("regex_tokenizer_"), ...) {
.args <- list(
input_col = input_col,
output_col = output_col,
gaps = gaps,
min_token_length = min_token_length,
pattern = pattern,
to_lower_case = to_lower_case,
uid = uid
) %>%
c(rlang::dots_list(...)) %>%
validator_ml_regex_tokenizer()
jobj <- spark_pipeline_stage(
x, "org.apache.spark.ml.feature.RegexTokenizer",
input_col = .args[["input_col"]], output_col = .args[["output_col"]], uid = .args[["uid"]]
) %>%
invoke("setGaps", .args[["gaps"]]) %>%
invoke("setMinTokenLength", .args[["min_token_length"]]) %>%
invoke("setPattern", .args[["pattern"]]) %>%
invoke("setToLowercase", .args[["to_lower_case"]])
new_ml_regex_tokenizer(jobj)
}
#' @export
ft_regex_tokenizer.ml_pipeline <- function(x, input_col = NULL, output_col = NULL, gaps = TRUE,
min_token_length = 1, pattern = "\\s+", to_lower_case = TRUE,
uid = random_string("regex_tokenizer_"), ...) {
stage <- ft_regex_tokenizer.spark_connection(
x = spark_connection(x),
input_col = input_col,
output_col = output_col,
gaps = gaps,
min_token_length = min_token_length,
pattern = pattern,
to_lower_case = to_lower_case,
uid = uid,
...
)
ml_add_stage(x, stage)
}
#' @export
ft_regex_tokenizer.tbl_spark <- function(x, input_col = NULL, output_col = NULL, gaps = TRUE,
min_token_length = 1, pattern = "\\s+", to_lower_case = TRUE,
uid = random_string("regex_tokenizer_"), ...) {
stage <- ft_regex_tokenizer.spark_connection(
x = spark_connection(x),
input_col = input_col,
output_col = output_col,
gaps = gaps,
min_token_length = min_token_length,
pattern = pattern,
to_lower_case = to_lower_case,
uid = uid,
...
)
ml_transform(stage, x)
}
new_ml_regex_tokenizer <- function(jobj) {
new_ml_transformer(jobj, class = "ml_regex_tokenizer")
}
validator_ml_regex_tokenizer <- function(.args) {
.args <- validate_args_transformer(.args)
.args[["gaps"]] <- cast_scalar_logical(.args[["gaps"]])
.args[["min_token_length"]] <- cast_scalar_integer(.args[["min_token_length"]])
.args[["pattern"]] <- cast_string(.args[["pattern"]])
.args[["to_lower_case"]] <- cast_scalar_logical(.args[["to_lower_case"]])
.args
}
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