Important note, when I have prep.step_hai_hyperbolic() loaded in the Global environment then prep() works just fine, so maybe prep.step_hai_hyperbolic() is not loading into the environment somehow or not getting properly called? I can call it via healthyR.ai::prep.step_hai_hyperbolic()
I am making a custom step function and am getting the following error:
> prep(rec_obj)
Error in UseMethod("prep") :
no applicable method for 'prep' applied to an object of class "c('step_hai_hyperbolic', 'step')"
Here is my code (not sure why but if I source the file, then things work...maybe something is not exporting?)
#' Recipes Step Hyperbolic Generator
#'
#' @family Recipes
#'
#' `step_hai_hyperbolic` creates a a *specification* of a recipe
#' step that will convert numeric data into either a 'sin', 'cos', or 'tan'
#' feature that can aid in machine learning.
#'
#' @param recipe A recipe object. The step will be added to the
#' sequence of operations for this recipe.
#' @param ... One or more selector functions to choose which
#' variables that will be used to create the new variables. The
#' selected variables should have class `numeric`
#' @param trained A logical to indicate if the quantities for
#' preprocessing have been estimated.
#' @param role For model terms created by this step, what analysis
#' role should they be assigned?. By default, the function assumes
#' that the new variable columns created by the original variables
#' will be used as predictors in a model.
#' @param columns A character string of variables that will be
#' used as inputs. This field is a placeholder and will be
#' populated once `recipes::prep()` is used.
#' @param scale_type A character string of a scaling type, one of "sin", "cos", or "tan"
#' @param skip A logical. Should the step be skipped when the recipe is
#' baked by bake.recipe()? While all operations are baked when prep.recipe()
#' is run, some operations may not be able to be conducted on new data
#' (e.g. processing the outcome variable(s)). Care should be taken when
#' using skip = TRUE as it may affect the computations for subsequent operations.
#' @param id A character string that is unique to this step to identify it.
#'
#' @return For `step_timeseries_signature`, an updated version of recipe with
#' the new step added to the sequence of existing steps (if any).
#' For the `tidy` method, a tibble with columns `terms`
#' (the selectors or variables selected), `value` (the feature
#' names).
#'
#' Main Recipe Functions:
#' - `recipes::recipe()`
#' - `recipes::prep()`
#' - `recipes::bake()`
#'
#'
#' @details
#'
#' __Numeric Variables__
#' Unlike other steps, `step_hai_hyperbolic` does *not*
#' remove the original numeric variables. [recipes::step_rm()] can be
#' used for this purpose.
#'
#' @examples
#' suppressPackageStartupMessages(library(dplyr))
#'
#' len_out = 10
#' by_unit = "month"
#' start_date = as.Date("2021-01-01")
#'
#' data_tbl <- tibble(
#' date_col = seq.Date(from = start_date, length.out = len_out, by = by_unit),
#' a = rnorm(len_out),
#' b = runif(len_out)
#' )
#'
#' # Create a recipe object
#' rec_obj <- recipe(a ~ ., data = data_tbl) %>%
#' step_hai_hyperbolic(b, scale_type = "sin") %>%
#' step_hai_hyperbolic(b, scale_type = "cos")
#'
#' # View the recipe object
#' rec_obj
#'
#' # Prepare the recipe object
#' prep(rec_obj)
#'
#' # Bake the recipe object - Adds the Time Series Signature
#' bake(prep(rec_obj), data_tbl)
#'
#' rec_obj %>% get_juiced_data()
#'
#' @export
#'
step_hai_hyperbolic <- function(recipe,
...,
role = "predictor",
trained = FALSE,
columns = NULL,
scale_type = c("sin","cos","tan"),
skip = FALSE,
id = rand_id("hai_hyperbolic")
){
terms <- recipes::ellipse_check(...)
funcs <- c("sin", "cos", "tan")
if (!(scale_type %in% funcs))
rlang::abort("`func` should be either `sin`, `cos`, or `tan`")
recipes::add_step(
recipe,
step_hai_hyperbolic_new(
terms = terms,
role = role,
trained = trained,
columns = columns,
scale_type = scale_type,
skip = skip,
id = id
)
)
}
step_hai_hyperbolic_new <-
function(terms, role, trained, columns, scale_type, skip, id){
recipes::step(
subclass = "hai_hyperbolic",
terms = terms,
role = role,
trained = trained,
columns = columns,
scale_type = scale_type,
skip = skip,
id = id
)
}
#' @export
prep.step_hai_hyperbolic <- function(x, training, info = NULL, ...) {
#col_names <- recipes::recipes_eval_select(x$terms, training, info = info)
col_names <- recipes::recipes_eval_select(x$terms, training, info)
value_data <- info[info$variable %in% col_names, ]
if(any(value_data$type != "numeric")){
rlang::abort(
paste0("All variables for `step_hai_hyperbolic` must be `numeric`",
"`integer` `double` classes.")
)
}
step_hai_hyperbolic_new(
terms = x$terms,
role = x$role,
trained = TRUE,
columns = col_names,
scale_type = x$scale_type,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_hai_hyperbolic <- function(object, new_data, ...){
make_call <- function(col, scale_type){
rlang::call2(
"hai_hyperbolic_vec",
x = rlang::sym(col)
, .scale_type = scale_type
, .ns = "healthyR.ai"
)
}
grid <- expand.grid(
col = object$columns
, scale_type = object$scale_type
, stringsAsFactors = FALSE
)
calls <- purrr::pmap(.l = list(grid$col, grid$scale_type), make_call)
# Column Names
newname <- paste0(grid$col, "_", grid$scale_type)
calls <- recipes::check_name(calls, new_data, object, newname, TRUE)
tibble::as_tibble(dplyr::mutate(new_data, !!!calls))
}
#' @export
print.step_hai_hyperbolic <-
function(x, width = max(20, options()$width - 35), ...) {
cat("Hyperbolic transformation on ", sep = "")
printer(
# Names before prep (could be selectors)
untr_obj = x$terms,
# Names after prep:
tr_obj = names(x$columns),
# Has it been prepped?
trained = x$trained,
# An estimate of how many characters to print on a line:
width = width
)
invisible(x)
}
#' @rdname required_pkgs.healthyR.ai
#' @export
required_pkgs.step_hai_hyperbolic <- function(x, ...) {
c("healthyR.ai")
}
Not sure what I am doing wrong