#' Feature Transformation -- ElementwiseProduct (Transformer)
#'
#' Outputs the Hadamard product (i.e., the element-wise product) of each input vector
#' with a provided "weight" vector. In other words, it scales each column of the
#' dataset by a scalar multiplier.
#'
#' @template roxlate-ml-feature-input-output-col
#' @template roxlate-ml-feature-transformer
#' @param scaling_vec the vector to multiply with input vectors
#'
#' @export
ft_elementwise_product <- function(x, input_col = NULL, output_col = NULL, scaling_vec = NULL,
uid = random_string("elementwise_product_"), ...) {
check_dots_used()
UseMethod("ft_elementwise_product")
}
ml_elementwise_product <- ft_elementwise_product
#' @export
ft_elementwise_product.spark_connection <- function(x, input_col = NULL, output_col = NULL, scaling_vec = NULL,
uid = random_string("elementwise_product_"), ...) {
spark_require_version(x, "2.0.0", "ElementwiseProduct")
.args <- list(
input_col = input_col,
output_col = output_col,
scaling_vec = scaling_vec,
uid = uid
) %>%
c(rlang::dots_list(...)) %>%
validator_ml_elementwise_product()
jobj <- spark_pipeline_stage(
x, "org.apache.spark.ml.feature.ElementwiseProduct",
input_col = .args[["input_col"]], output_col = .args[["output_col"]], uid = .args[["uid"]]
)
if (!is.null(.args[["scaling_vec"]])) {
jobj <- invoke_static(x, "sparklyr.MLUtils2", "setScalingVec", jobj, .args[["scaling_vec"]])
}
new_ml_elementwise_product(jobj)
}
#' @export
ft_elementwise_product.ml_pipeline <- function(x, input_col = NULL, output_col = NULL, scaling_vec = NULL,
uid = random_string("elementwise_product_"), ...) {
transformer <- ft_elementwise_product.spark_connection(
x = spark_connection(x),
input_col = input_col,
output_col = output_col,
scaling_vec = scaling_vec,
uid = uid,
...
)
ml_add_stage(x, transformer)
}
#' @export
ft_elementwise_product.tbl_spark <- function(x, input_col = NULL, output_col = NULL, scaling_vec = NULL,
uid = random_string("elementwise_product_"), ...) {
transformer <- ft_elementwise_product.spark_connection(
x = spark_connection(x),
input_col = input_col,
output_col = output_col,
scaling_vec = scaling_vec,
uid = uid,
...
)
ml_transform(transformer, x)
}
new_ml_elementwise_product <- function(jobj) {
new_ml_transformer(jobj, class = "ml_elementwise_product")
}
# ElementwiseProduct
validator_ml_elementwise_product <- function(.args) {
.args <- validate_args_transformer(.args)
.args[["scaling_vec"]] <- cast_double_list(.args[["scaling_vec"]], allow_null = TRUE)
.args
}
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