#' Feature Transformation -- StringIndexer (Estimator)
#'
#' A label indexer that maps a string column of labels to an ML column of
#' label indices. If the input column is numeric, we cast it to string and
#' index the string values. The indices are in \code{[0, numLabels)}, ordered by
#' label frequencies. So the most frequent label gets index 0. This function
#' is the inverse of \code{\link{ft_index_to_string}}.
#'
#' @template roxlate-ml-feature-input-output-col
#' @template roxlate-ml-feature-transformer
#' @template roxlate-ml-feature-estimator-transformer
#' @template roxlate-ml-feature-handle-invalid
#' @param string_order_type (Spark 2.3+)How to order labels of string column.
#' The first label after ordering is assigned an index of 0. Options are
#' \code{"frequencyDesc"}, \code{"frequencyAsc"}, \code{"alphabetDesc"}, and \code{"alphabetAsc"}.
#' Defaults to \code{"frequencyDesc"}.
#' @seealso \code{\link{ft_index_to_string}}
#' @export
ft_string_indexer <- function(x, input_col = NULL, output_col = NULL,
handle_invalid = "error", string_order_type = "frequencyDesc",
uid = random_string("string_indexer_"), ...) {
check_dots_used()
UseMethod("ft_string_indexer")
}
ml_string_indexer <- ft_string_indexer
#' @export
ft_string_indexer.spark_connection <- function(x, input_col = NULL, output_col = NULL,
handle_invalid = "error", string_order_type = "frequencyDesc",
uid = random_string("string_indexer_"), ...) {
.args <- list(
input_col = input_col,
output_col = output_col,
handle_invalid = handle_invalid,
string_order_type = string_order_type,
uid = uid
) %>%
validator_ml_string_indexer()
estimator <- spark_pipeline_stage(
x, "org.apache.spark.ml.feature.StringIndexer",
input_col = .args[["input_col"]], output_col = .args[["output_col"]], uid = .args[["uid"]]
) %>%
jobj_set_param("setHandleInvalid", .args[["handle_invalid"]], "2.1.0", "error") %>%
jobj_set_param("setStringOrderType", .args[["string_order_type"]], "2.3.0", "frequencyDesc") %>%
new_ml_string_indexer()
estimator
}
#' @export
ft_string_indexer.ml_pipeline <- function(x, input_col = NULL, output_col = NULL,
handle_invalid = "error", string_order_type = "frequencyDesc",
uid = random_string("string_indexer_"), ...) {
stage <- ft_string_indexer.spark_connection(
x = spark_connection(x),
input_col = input_col,
output_col = output_col,
handle_invalid = handle_invalid,
string_order_type = string_order_type,
uid = uid,
...
)
ml_add_stage(x, stage)
}
#' @export
ft_string_indexer.tbl_spark <- function(x, input_col = NULL, output_col = NULL,
handle_invalid = "error", string_order_type = "frequencyDesc",
uid = random_string("string_indexer_"), ...) {
stage <- ft_string_indexer.spark_connection(
x = spark_connection(x),
input_col = input_col,
output_col = output_col,
handle_invalid = handle_invalid,
string_order_type = string_order_type,
uid = uid,
...
)
# backwards compatibility for params argument
dots <- rlang::dots_list(...)
if (rlang::has_name(dots, "params") && is.environment(dots$params)) {
warning("`params` has been deprecated and will be removed in a future release.", call. = FALSE)
transformer <- if (is_ml_transformer(stage)) {
stage
} else {
ml_fit(stage, x)
}
dots$params$labels <- spark_jobj(transformer) %>%
invoke("labels") %>%
as.character()
transformer %>%
ml_transform(x)
} else {
if (is_ml_transformer(stage)) {
ml_transform(stage, x)
} else {
ml_fit_and_transform(stage, x)
}
}
}
new_ml_string_indexer <- function(jobj) {
new_ml_estimator(jobj, class = "ml_string_indexer")
}
new_ml_string_indexer_model <- function(jobj) {
new_ml_transformer(jobj,
labels = invoke(jobj, "labels") %>%
as.character(),
class = "ml_string_indexer_model"
)
}
#' @rdname ft_string_indexer
#' @param model A fitted StringIndexer model returned by \code{ft_string_indexer()}
#' @return \code{ml_labels()} returns a vector of labels, corresponding to indices to be assigned.
#' @export
ml_labels <- function(model) model$labels
validator_ml_string_indexer <- function(.args) {
.args <- validate_args_transformer(.args)
.args[["handle_invalid"]] <- cast_choice(
.args[["handle_invalid"]], c("error", "skip", "keep")
)
.args[["string_order_type"]] <- cast_choice(
.args[["string_order_type"]],
c("frequencyDesc", "frequencyAsc", "alphabetDesc", "alphabetAsc")
)
.args
}
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