#' Feature Transformation -- MinMaxScaler (Estimator)
#'
#' Rescale each feature individually to a common range [min, max] linearly using
#' column summary statistics, which is also known as min-max normalization or
#' Rescaling
#'
#' @template roxlate-ml-feature-input-output-col
#' @template roxlate-ml-feature-transformer
#' @template roxlate-ml-feature-estimator-transformer
#' @param max Upper bound after transformation, shared by all features Default: 1.0
#' @param min Lower bound after transformation, shared by all features Default: 0.0
#'
#' @examples
#' \dontrun{
#' sc <- spark_connect(master = "local")
#' iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
#'
#' features <- c("Sepal_Length", "Sepal_Width", "Petal_Length", "Petal_Width")
#'
#' iris_tbl %>%
#' ft_vector_assembler(
#' input_col = features,
#' output_col = "features_temp"
#' ) %>%
#' ft_min_max_scaler(
#' input_col = "features_temp",
#' output_col = "features"
#' )
#' }
#'
#' @export
ft_min_max_scaler <- function(x, input_col = NULL, output_col = NULL,
min = 0, max = 1,
uid = random_string("min_max_scaler_"), ...) {
check_dots_used()
UseMethod("ft_min_max_scaler")
}
ml_min_max_scaler <- ft_min_max_scaler
#' @export
ft_min_max_scaler.spark_connection <- function(x, input_col = NULL, output_col = NULL,
min = 0, max = 1,
uid = random_string("min_max_scaler_"), ...) {
.args <- list(
input_col = input_col,
output_col = output_col,
min = min,
max = max,
uid = uid
) %>%
c(rlang::dots_list(...)) %>%
validator_ml_min_max_scaler()
estimator <- spark_pipeline_stage(
x, "org.apache.spark.ml.feature.MinMaxScaler",
input_col = .args[["input_col"]], output_col = .args[["output_col"]], uid = .args[["uid"]]
) %>%
invoke("setMin", .args[["min"]]) %>%
invoke("setMax", .args[["max"]]) %>%
new_ml_min_max_scaler()
estimator
}
#' @export
ft_min_max_scaler.ml_pipeline <- function(x, input_col = NULL, output_col = NULL,
min = 0, max = 1,
uid = random_string("min_max_scaler_"), ...) {
stage <- ft_min_max_scaler.spark_connection(
x = spark_connection(x),
input_col = input_col,
output_col = output_col,
min = min,
max = max,
uid = uid,
...
)
ml_add_stage(x, stage)
}
#' @export
ft_min_max_scaler.tbl_spark <- function(x, input_col = NULL, output_col = NULL,
min = 0, max = 1,
uid = random_string("min_max_scaler_"), ...) {
stage <- ft_min_max_scaler.spark_connection(
x = spark_connection(x),
input_col = input_col,
output_col = output_col,
min = min,
max = max,
uid = uid,
...
)
if (is_ml_transformer(stage)) {
ml_transform(stage, x)
} else {
ml_fit_and_transform(stage, x)
}
}
new_ml_min_max_scaler <- function(jobj) {
new_ml_estimator(jobj, class = "ml_min_max_scaler")
}
new_ml_min_max_scaler_model <- function(jobj) {
new_ml_transformer(jobj, class = "ml_min_max_scaler_model")
}
validator_ml_min_max_scaler <- function(.args) {
.args <- validate_args_transformer(.args)
.args[["min"]] <- cast_scalar_double(.args[["min"]])
.args[["max"]] <- cast_scalar_double(.args[["max"]])
.args
}
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