#' Feature Transformation -- PolynomialExpansion (Transformer)
#'
#' Perform feature expansion in a polynomial space. E.g. take a 2-variable feature
#' vector as an example: (x, y), if we want to expand it with degree 2, then
#' we get (x, x * x, y, x * y, y * y).
#'
#' @template roxlate-ml-feature-input-output-col
#' @template roxlate-ml-feature-transformer
#'
#' @param degree The polynomial degree to expand, which should be greater
#' than equal to 1. A value of 1 means no expansion. Default: 2
#' @export
ft_polynomial_expansion <- function(x, input_col = NULL, output_col = NULL,
degree = 2, uid = random_string("polynomial_expansion_"), ...) {
check_dots_used()
UseMethod("ft_polynomial_expansion")
}
ml_polynomial_expansion <- ft_polynomial_expansion
#' @export
ft_polynomial_expansion.spark_connection <- function(x, input_col = NULL, output_col = NULL,
degree = 2, uid = random_string("polynomial_expansion_"), ...) {
.args <- list(
input_col = input_col,
output_col = output_col,
degree = degree,
uid = uid
) %>%
c(rlang::dots_list(...)) %>%
validator_ml_polynomial_expansion()
jobj <- spark_pipeline_stage(
x, "org.apache.spark.ml.feature.PolynomialExpansion",
input_col = .args[["input_col"]], output_col = .args[["output_col"]], uid = .args[["uid"]]
) %>%
invoke("setDegree", .args[["degree"]])
new_ml_polynomial_expansion(jobj)
}
#' @export
ft_polynomial_expansion.ml_pipeline <- function(x, input_col = NULL, output_col = NULL,
degree = 2, uid = random_string("polynomial_expansion_"), ...) {
stage <- ft_polynomial_expansion.spark_connection(
x = spark_connection(x),
input_col = input_col,
output_col = output_col,
degree = degree,
uid = uid,
...
)
ml_add_stage(x, stage)
}
#' @export
ft_polynomial_expansion.tbl_spark <- function(x, input_col = NULL, output_col = NULL,
degree = 2, uid = random_string("polynomial_expansion_"), ...) {
stage <- ft_polynomial_expansion.spark_connection(
x = spark_connection(x),
input_col = input_col,
output_col = output_col,
degree = degree,
uid = uid,
...
)
ml_transform(stage, x)
}
new_ml_polynomial_expansion <- function(jobj) {
new_ml_transformer(jobj, class = "ml_polynomial_expansion")
}
validator_ml_polynomial_expansion <- function(.args) {
.args <- validate_args_transformer(.args)
.args[["degree"]] <- cast_scalar_integer(.args[["degree"]])
if (.args[["degree"]] < 1) stop("`degree` must be greater than 1.", call. = FALSE)
.args
}
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