#' Feature Transformation -- Binarizer (Transformer)
#'
#' Apply thresholding to a column, such that values less than or equal to the
#' \code{threshold} are assigned the value 0.0, and values greater than the
#' threshold are assigned the value 1.0. Column output is numeric for
#' compatibility with other modeling functions.
#'
#' @template roxlate-ml-feature-input-output-col
#' @template roxlate-ml-feature-transformer
#'
#' @param threshold Threshold used to binarize continuous features.
#'
#' @examples
#' \dontrun{
#' library(dplyr)
#'
#' sc <- spark_connect(master = "local")
#' iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
#'
#' iris_tbl %>%
#' ft_binarizer(
#' input_col = "Sepal_Length",
#' output_col = "Sepal_Length_bin",
#' threshold = 5
#' ) %>%
#' select(Sepal_Length, Sepal_Length_bin, Species)
#' }
#'
#' @export
ft_binarizer <- function(x, input_col, output_col, threshold = 0, uid = random_string("binarizer_"), ...) {
check_dots_used()
UseMethod("ft_binarizer")
}
ml_binarizer <- ft_binarizer
#' @export
ft_binarizer.spark_connection <- function(x, input_col = NULL, output_col = NULL, threshold = 0,
uid = random_string("binarizer_"), ...) {
.args <- list(
input_col = input_col,
output_col = output_col,
threshold = threshold,
uid = uid
) %>%
c(rlang::dots_list(...)) %>%
validator_ml_binarizer()
jobj <- spark_pipeline_stage(
x, "org.apache.spark.ml.feature.Binarizer",
input_col = .args[["input_col"]],
output_col = .args[["output_col"]],
uid = .args[["uid"]]
) %>%
invoke("setThreshold", .args[["threshold"]])
new_ml_binarizer(jobj)
}
#' @export
ft_binarizer.ml_pipeline <- function(x, input_col = NULL, output_col = NULL, threshold = 0,
uid = random_string("binarizer_"), ...) {
stage <- ft_binarizer.spark_connection(
x = spark_connection(x),
input_col = input_col,
output_col = output_col,
threshold = threshold,
uid = uid,
...
)
ml_add_stage(x, stage)
}
#' @export
ft_binarizer.tbl_spark <- function(x, input_col = NULL, output_col = NULL, threshold = 0,
uid = random_string("binarizer_"), ...) {
stage <- ft_binarizer.spark_connection(
x = spark_connection(x),
input_col = input_col,
output_col = output_col,
threshold = threshold,
uid = uid,
...
)
ml_transform(stage, x)
}
new_ml_binarizer <- function(jobj) {
new_ml_transformer(jobj, class = "ml_binarizer")
}
validator_ml_binarizer <- function(.args) {
.args <- validate_args_transformer(.args)
.args[["threshold"]] <- cast_scalar_double(.args[["threshold"]])
.args
}
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