spsanderson
commented
2 years ago Start of function
#' Data Preprocessor - Transformation Functions
#'
#' @family Data Recipes
#' @family Preprocessor
#'
#' @keywords internal
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @description
#' Takes in a recipe and will perform the desired transformation on the selected
#' varialbe(s) using a selected recipe. To call the desired transformation
#' recipe use a quoted argument like "boxcos", "bs" etc. This function
#' is not exported but may be called via the ::: method.
#'
#' @details
#' This function will get your data ready for processing with many types of ml/ai
#' models.
#'
#' This is intended to be used inside of the data processor and
#' therefore is an internal function. This documentation exists to explain the process
#' and help the user understand the parameters that can be set in the pre-processor function.
#'
#' [recipes::step_BoxCox()]
#' @seealso \url{https://recipes.tidymodels.org/reference/step_BoxCox.html}
#'
#' [recipes::step_bs()]
#' @seealso \url{https://recipes.tidymodels.org/reference/step_bs.html}
#'
#' [recipes::step_log()]
#' @seealso \url{https://recipes.tidymodels.org/reference/step_log.html}
#'
#' [recipes::step_logit()]
#' @seealso \url{https://recipes.tidymodels.org/reference/step_logit.html}
#'
#' [recipes::step_ns()]
#' @seealso \url{https://recipes.tidymodels.org/reference/step_ns.html}
#'
#' [recipes::step_relu()]
#' @seealso \url{https://recipes.tidymodels.org/reference/step_relu.html}
#'
#' [recipes::step_sqrt()]
#' @seealso \url{https://recipes.tidymodels.org/reference/step_sqrt.html}
#'
#' [recipes::step_YeoJohnson()]
#' @seealso \url{https://recipes.tidymodels.org/reference/step_YeoJohnson.html}
#'
#' @param .recipe_object The data that you want to process
#' @param ... One or more selector functions to choose variables to be imputed.
#' When used with imp_vars, these dots indicate which variables are used to
#' predict the missing data in each variable. See selections() for more details
#' @param .type_of_scale This is a quoted argument and can be one of the following:
#' - "boxcox"
#' - "bs"
#' - "log"
#' - "logit"
#' - "ns"
#' - "relu"
#' - "sqrt"
#' - "yeojohnson
#' @param .bc_limits A length 2 numeric vector defining the range to compute the
#' transformation parameter lambda.
#' @param .bc_num_unique An integer to specify minimum required unique values to
#' evaluate for a transformation
#' @param .bs_deg_free The degrees of freedom for the spline. As the degrees of
#' freedom for a spline increase, more flexible and complex curves can be
#' generated. When a single degree of freedom is used, the result is a rescaled
#' version of the original data.
#' @param .bs_degree Degree of polynomial spline (integer).
#' @param .log_base A numberic value for the base.
#' @param .logit_offset A numberic value to modify values ofthe columns that are
#' either one or zero. They are modifed to be `x - offset` or `offset` respectively.
#' @param .ns_deg_free The degrees of freedom for the natural spline. As the
#' degrees of freedom for a natural spline increase, more flexible and complex
#' curves can be generated. When a single degree of freedom is used, the result
#' is a rescaled version of the original data.
#' @param .rel_shift A numeric value dictating a translation to apply to the data.
#' @param .rel_reverse A logical to indicate if theleft hinge should be used as
#' opposed to the right hinge.
#' @param .rel_smooth A logical indicating if hte softplus function, a smooth
#' approximation to the rectified linear transformation, should be used.
#' @param .yj_limits A length 2 numeric vector defining the range to compute the
#' transformation parameter lambda.
#' @param .yj_num_unique An integer where data that have less possible values
#' will not be evaluated for a transformation.
#'
#' @examples
#' suppressPackageStartupMessages(library(dplyr))
#' suppressPackageStartupMessages(library(recipes))
#'
#' date_seq <- seq.Date(from = as.Date("2013-01-01"), length.out = 100, by = "month")
#' val_seq <- rep(rnorm(10, mean = 6, sd = 2), times = 10)
#' df_tbl <- tibble(
#' date_col = date_seq,
#' value = val_seq
#' )
#'
#' rec_obj <- recipe(value ~., df_tbl)
#'
#' healthyR.ai:::hai_step_transform(
#' .recipe_object = rec_obj,
#' value,
#' .type_of_scale = "log"
#' )$scale_rec_obj %>%
#' get_juiced_data()
#'
#' @return
#' A list object
#'
hai_step_transform <- function(.recipe_object = NULL, ..., .type_of_scale = "log"
, .bc_limits, .bc_num_unique, .bs_deg_free, .bs_degree
, .log_base, .logit_offset, .ns_deg_free
, .rel_shift, .rel_reverse, .rel_smooth
, .yj_limits, .yj_num_unique){
# Make sure a recipe was passed
if(is.null(.recipe_object)){
rlang::abort("`.recipe_object` must be passed, please add.")
} else {
rec_obj <- .recipe_object
}
# * Parameters ----
terms <- rlang::enquos(...)
scale_type <- as.character(.type_of_scale)
inverse_bool <- as.logical(.inverse)
# * Checks ----
if(!tolower(scale_type) %in% c(
"boxcox","bs","log","logit","ns","relu","sqrt","yeojohnson"
)
){
stop(call. = FALSE, "(.type_of_scale) is not implemented. Please choose
from 'boxcox','bs','log','logit','ns','relu','sqrt','yeojohnson'")
}
# If Statement to get the recipe desired ----
if(scale_type == "boxcox"){
scale_obj <- recipes::step_BoxCox(
recipe = rec_obj,
limits = .bc_limits,
num_unique = .bc_num_unique,
!!! terms
)
} else if(scale_type == "bs"){
scale_obj <- recipes::step_bs(
recipe = rec_obj,
deg_free = .bs_deg_free,
degree = .bs_degree,
!!! terms
)
} else if(scale_type == "log"){
scale_obj <- recipes::step_log(
recipe = rec_obj,
base = .log_base,
!!! terms
)
} else if(scale_type == "logit"){
scale_obj <- recipes::step_logit(
recipe = rec_obj,
offset = .logit_offset,
!!! terms
)
} else if(scale_type == "ns"){
scale_obj <- recipes::step_ns(
recipe = rec_obj,
deg_free = .ns_deg_free,
!!! terms
)
} else if(scale_type == "relu"){
scale_obj <- recipes::step_relu(
recipe = rec_obj,
shift = .rel_shift,
reverse = .rel_reverse,
smooth = .rel_smooth,
!!! terms
)
} else if(scale_type == "sqrt"){
scale_obj <- recipes::step_sqrt(
recipe = rec_obj,
!!! terms
)
} else if(scale_type == "yeojohnson"){
scale_obj <- recipes::step_YeoJohnson(
recipe = rec_obj,
limits = .yj_limits,
num_unique = .yj_num_unique,
!!! terms
)
}
# * Recipe List ---
output <- list(
rec_base = rec_obj,
scale_rec_obj = scale_obj
)
# * Return ----
return(output)
}
https://pycaret.org/transformation/