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#' Center and scale numeric data
#'
#' `step_normalize` creates a *specification* of a recipe
#' step that will normalize numeric data to have a standard
#' deviation of one and a mean of zero.
#'
#' @inheritParams step_center
#' @param means A named numeric vector of means. This is `NULL` until computed
#' by [prep()].
#' @param sds A named numeric vector of standard deviations This is `NULL` until
#' computed by [prep()].
#' @param na_rm A logical value indicating whether `NA` values should be removed
#' when computing the standard deviation and mean.
#' @template step-return
#' @family normalization steps
#' @export
#' @details Centering data means that the average of a variable is subtracted
#' from the data. Scaling data means that the standard deviation of a variable
#' is divided out of the data. `step_normalize` estimates the variable standard
#' deviations and means from the data used in the `training` argument of
#' `prep.recipe`. [`bake.recipe`] then applies the scaling to new data sets using
#' these estimates.
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble with columns
#' `terms` (the selectors or variables selected), `value` (the standard
#' deviations and means), and `statistic` for the type of value is
#' returned.
#'
#' @template case-weights-unsupervised
#'
#' @examplesIf rlang::is_installed("modeldata")
#' data(biomass, package = "modeldata")
#'
#' biomass_tr <- biomass[biomass$dataset == "Training", ]
#' biomass_te <- biomass[biomass$dataset == "Testing", ]
#'
#' rec <- recipe(
#' HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
#' data = biomass_tr
#' )
#'
#' norm_trans <- rec %>%
#' step_normalize(carbon, hydrogen)
#'
#' norm_obj <- prep(norm_trans, training = biomass_tr)
#'
#' transformed_te <- bake(norm_obj, biomass_te)
#'
#' biomass_te[1:10, names(transformed_te)]
#' transformed_te
#' tidy(norm_trans, number = 1)
#' tidy(norm_obj, number = 1)
#'
#' # To keep the original variables in the output, use `step_mutate_at`:
#' norm_keep_orig <- rec %>%
#' step_mutate_at(all_numeric_predictors(), fn = list(orig = ~.)) %>%
#' step_normalize(-contains("orig"), -all_outcomes())
#'
#' keep_orig_obj <- prep(norm_keep_orig, training = biomass_tr)
#' keep_orig_te <- bake(keep_orig_obj, biomass_te)
#' keep_orig_te
step_normalize <-
function(recipe,
...,
role = NA,
trained = FALSE,
means = NULL,
sds = NULL,
na_rm = TRUE,
skip = FALSE,
id = rand_id("normalize")) {
add_step(
recipe,
step_normalize_new(
terms = enquos(...),
role = role,
trained = trained,
means = means,
sds = sds,
na_rm = na_rm,
skip = skip,
id = id,
case_weights = NULL
)
)
}
step_normalize_new <-
function(terms, role, trained, means, sds, na_rm, skip, id, case_weights) {
step(
subclass = "normalize",
terms = terms,
role = role,
trained = trained,
means = means,
sds = sds,
na_rm = na_rm,
skip = skip,
id = id,
case_weights = case_weights
)
}
sd_check <- function(x) {
zero_sd <- which(x < .Machine$double.eps)
if (length(zero_sd) > 0) {
glue_cols <- glue::glue_collapse(
glue::glue("`{names(zero_sd)}`"), sep = ", ", last = " and "
)
rlang::warn(
glue::glue(
"Column(s) have zero variance so scaling cannot be used: {glue_cols}. ",
"Consider using `step_zv()` to remove those columns before normalizing"
)
)
x[zero_sd] <- 1
}
x
}
#' @export
prep.step_normalize <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("double", "integer"))
wts <- get_case_weights(info, training)
were_weights_used <- are_weights_used(wts, unsupervised = TRUE)
if (isFALSE(were_weights_used)) {
wts <- NULL
}
means <- averages(training[, col_names], wts, na_rm = x$na_rm)
vars <- variances(training[, col_names], wts, na_rm = x$na_rm)
sds <- sqrt(vars)
sds <- sd_check(sds)
step_normalize_new(
terms = x$terms,
role = x$role,
trained = TRUE,
means = means,
sds = sds,
na_rm = x$na_rm,
skip = x$skip,
id = x$id,
case_weights = were_weights_used
)
}
#' @export
bake.step_normalize <- function(object, new_data, ...) {
check_new_data(names(object$means), object, new_data)
for (column in names(object$means)) {
mean <- object$means[column]
sd <- object$sds[column]
new_data[[column]] <- (new_data[[column]] - mean) / sd
}
new_data
}
print.step_normalize <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Centering and scaling for "
print_step(names(x$sds), x$terms, x$trained, title, width,
case_weights = x$case_weights)
invisible(x)
}
#' @rdname tidy.recipe
#' @export
tidy.step_normalize <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(
terms = c(names(x$means), names(x$sds)),
statistic = rep(c("mean", "sd"), each = length(x$sds)),
value = unname(c(x$means, x$sds))
)
} else {
term_names <- sel2char(x$terms)
res <- tibble(
terms = term_names,
statistic = na_chr,
value = na_dbl
)
}
res$id <- x$id
res
}
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