#' Default recipe blueprint
#'
#' This pages holds the details for the recipe preprocessing blueprint. This
#' is the blueprint used by default from `mold()` if `x` is a recipe.
#'
#' @inheritParams new_recipe_blueprint
#'
#' @param x An unprepped recipe created from [recipes::recipe()].
#'
#' @param data A data frame or matrix containing the outcomes and predictors.
#'
#' @param blueprint A preprocessing `blueprint`. If left as `NULL`, then a
#' [default_recipe_blueprint()] is used.
#'
#' @param ... Not used.
#'
#' @return
#'
#' For `default_recipe_blueprint()`, a recipe blueprint.
#'
#' @section Mold:
#'
#' When `mold()` is used with the default recipe blueprint:
#'
#' - It calls [recipes::prep()] to prep the recipe.
#'
#' - It calls [recipes::juice()] to extract the outcomes and predictors. These
#' are returned as tibbles.
#'
#' - If `intercept = TRUE`, adds an intercept column to the predictors.
#'
#' @section Forge:
#'
#' When `forge()` is used with the default recipe blueprint:
#'
#' - It calls [shrink()] to trim `new_data` to only the required columns and
#' coerce `new_data` to a tibble.
#'
#' - It calls [scream()] to perform validation on the structure of the columns
#' of `new_data`.
#'
#' - It calls [recipes::bake()] on the `new_data` using the prepped recipe
#' used during training.
#'
#' - It adds an intercept column onto `new_data` if `intercept = TRUE`.
#'
#' @export
#' @examples
#' library(recipes)
#'
#' # ---------------------------------------------------------------------------
#' # Setup
#'
#' train <- iris[1:100, ]
#' test <- iris[101:150, ]
#'
#' # ---------------------------------------------------------------------------
#' # Recipes example
#'
#' # Create a recipe that logs a predictor
#' rec <- recipe(Species ~ Sepal.Length + Sepal.Width, train) %>%
#' step_log(Sepal.Length)
#'
#' processed <- mold(rec, train)
#'
#' # Sepal.Length has been logged
#' processed$predictors
#'
#' processed$outcomes
#'
#' # The underlying blueprint is a prepped recipe
#' processed$blueprint$recipe
#'
#' # Call forge() with the blueprint and the test data
#' # to have it preprocess the test data in the same way
#' forge(test, processed$blueprint)
#'
#' # Use `outcomes = TRUE` to also extract the preprocessed outcome!
#' # This logged the Sepal.Length column of `new_data`
#' forge(test, processed$blueprint, outcomes = TRUE)
#'
#' # ---------------------------------------------------------------------------
#' # With an intercept
#'
#' # You can add an intercept with `intercept = TRUE`
#' processed <- mold(rec, train, blueprint = default_recipe_blueprint(intercept = TRUE))
#'
#' processed$predictors
#'
#' # But you also could have used a recipe step
#' rec2 <- step_intercept(rec)
#'
#' mold(rec2, iris)$predictors
#'
#' # ---------------------------------------------------------------------------
#' # Matrix output for predictors
#'
#' # You can change the `composition` of the predictor data set
#' bp <- default_recipe_blueprint(composition = "dgCMatrix")
#' processed <- mold(rec, train, blueprint = bp)
#' class(processed$predictors)
#'
#' # ---------------------------------------------------------------------------
#' # Non standard roles
#'
#' # If you have custom recipes roles, they are assumed to be required at
#' # `bake()` time when passing in `new_data`. This is an assumption that both
#' # recipes and hardhat makes, meaning that those roles are required at
#' # `forge()` time as well.
#' rec_roles <- recipe(train) %>%
#' update_role(Sepal.Width, new_role = "predictor") %>%
#' update_role(Species, new_role = "outcome") %>%
#' update_role(Sepal.Length, new_role = "id") %>%
#' update_role(Petal.Length, new_role = "important")
#'
#' processed_roles <- mold(rec_roles, train)
#'
#' # The custom roles will be in the `mold()` result in case you need
#' # them for modeling.
#' processed_roles$extras
#'
#' # And they are in the `forge()` result
#' forge(test, processed_roles$blueprint)$extras
#'
#' # If you remove a column with a custom role from the test data, then you
#' # won't be able to `forge()` even though this recipe technically didn't
#' # use that column in any steps
#' test2 <- test
#' test2$Petal.Length <- NULL
#' try(forge(test2, processed_roles$blueprint))
#'
#' # Most of the time, if you find yourself in the above scenario, then we
#' # suggest that you remove `Petal.Length` from the data that is supplied to
#' # the recipe. If that isn't an option, you can declare that that column
#' # isn't required at `bake()` time by using `update_role_requirements()`
#' rec_roles <- update_role_requirements(rec_roles, "important", bake = FALSE)
#' processed_roles <- mold(rec_roles, train)
#' forge(test2, processed_roles$blueprint)
default_recipe_blueprint <- function(intercept = FALSE,
allow_novel_levels = FALSE,
fresh = TRUE,
strings_as_factors = TRUE,
composition = "tibble") {
new_default_recipe_blueprint(
intercept = intercept,
allow_novel_levels = allow_novel_levels,
fresh = fresh,
strings_as_factors = strings_as_factors,
composition = composition
)
}
#' @param extra_role_ptypes A named list. The names are the unique non-standard
#' recipe roles (i.e. everything except `"predictors"` and `"outcomes"`). The
#' values are prototypes of the original columns with that role. These are
#' used for validation in `forge()`.
#'
#' @rdname new-default-blueprint
#' @export
new_default_recipe_blueprint <- function(intercept = FALSE,
allow_novel_levels = FALSE,
fresh = TRUE,
strings_as_factors = TRUE,
composition = "tibble",
ptypes = NULL,
recipe = NULL,
extra_role_ptypes = NULL,
...,
subclass = character()) {
new_recipe_blueprint(
intercept = intercept,
allow_novel_levels = allow_novel_levels,
fresh = fresh,
strings_as_factors = strings_as_factors,
composition = composition,
ptypes = ptypes,
recipe = recipe,
extra_role_ptypes = extra_role_ptypes,
...,
subclass = c(subclass, "default_recipe_blueprint")
)
}
#' @export
refresh_blueprint.default_recipe_blueprint <- function(blueprint) {
do.call(new_default_recipe_blueprint, as.list(blueprint))
}
# ------------------------------------------------------------------------------
#' @rdname run-mold
#' @export
run_mold.default_recipe_blueprint <- function(blueprint, ..., data) {
check_dots_empty0(...)
cleaned <- mold_recipe_default_clean(blueprint = blueprint, data = data)
blueprint <- cleaned$blueprint
data <- cleaned$data
mold_recipe_default_process(blueprint = blueprint, data = data)
}
# ------------------------------------------------------------------------------
# mold - recipe - clean
mold_recipe_default_clean <- function(blueprint, data) {
check_data_frame_or_matrix(data)
data <- coerce_to_tibble(data)
new_mold_clean(blueprint, data)
}
# ------------------------------------------------------------------------------
# mold - recipe - process
mold_recipe_default_process <- function(blueprint, data) {
# `prep()` will warn if you pass `training` data and `fresh = FALSE`
if (is_true(blueprint$fresh)) {
training <- data
} else {
training <- NULL
}
# Prep for predictors and outcomes
recipe <- recipes::prep(
blueprint$recipe,
training = training,
fresh = blueprint$fresh,
strings_as_factors = blueprint_strings_as_factors(blueprint)
)
blueprint <- update_blueprint0(blueprint, recipe = recipe)
processed <- mold_recipe_default_process_predictors(blueprint = blueprint, data = data)
blueprint <- processed$blueprint
predictors <- processed$data
predictors_ptype <- processed$ptype
predictors_extras <- processed$extras
processed <- mold_recipe_default_process_outcomes(blueprint = blueprint, data = data)
blueprint <- processed$blueprint
outcomes <- processed$data
outcomes_ptype <- processed$ptype
outcomes_extras <- processed$extras
processed <- mold_recipe_default_process_extras(blueprint, data)
blueprint <- processed$blueprint
extras <- processed$extras
extras <- c(
extras,
new_extras(predictors_extras, outcomes_extras)
)
# un-retain training data
blueprint <- update_blueprint0(blueprint, recipe = compost(blueprint$recipe))
ptypes <- new_ptypes(predictors_ptype, outcomes_ptype)
blueprint <- update_blueprint0(blueprint, ptypes = ptypes)
new_mold_process(predictors, outcomes, blueprint, extras)
}
mold_recipe_default_process_predictors <- function(blueprint, data) {
all_predictors <- recipes::all_predictors
predictors <- recipes::juice(blueprint$recipe, all_predictors())
predictors <- maybe_add_intercept_column(predictors, blueprint$intercept)
predictors <- recompose(predictors, composition = blueprint$composition)
ptype <- get_original_predictor_ptype(blueprint$recipe, data)
new_mold_process_terms(
blueprint = blueprint,
data = predictors,
ptype = ptype
)
}
mold_recipe_default_process_outcomes <- function(blueprint, data) {
all_outcomes <- recipes::all_outcomes
outcomes <- recipes::juice(blueprint$recipe, all_outcomes())
ptype <- get_original_outcome_ptype(blueprint$recipe, data)
new_mold_process_terms(
blueprint = blueprint,
data = outcomes,
ptype = ptype
)
}
mold_recipe_default_process_extras <- function(blueprint, data) {
# Capture original non standard role columns that exist in `data` and are also
# required by the `recipe$requirements$bake` requirement. These columns are
# also required in `new_data` at `bake()` time.
original_extra_role_cols <- get_extra_role_columns_original(
blueprint$recipe,
data
)
if (!is.null(original_extra_role_cols)) {
original_extra_role_ptypes <- lapply(original_extra_role_cols, extract_ptype)
blueprint <- update_blueprint0(
blueprint,
extra_role_ptypes = original_extra_role_ptypes
)
}
# Return all of the processed non standard role columns.
# These might be generated by `prep()` and could differ from the ones in the
# original data.
# These are not required in `new_data`, but we return them assuming the
# developer may need them for model fitting purposes.
processed_extra_role_cols <- get_extra_role_columns_processed(
blueprint$recipe,
recipes::juice(blueprint$recipe)
)
list(
blueprint = blueprint,
extras = list(roles = processed_extra_role_cols)
)
}
# ------------------------------------------------------------------------------
#' @rdname run-forge
#' @export
run_forge.default_recipe_blueprint <- function(blueprint,
new_data,
...,
outcomes = FALSE) {
check_dots_empty0(...)
cleaned <- forge_recipe_default_clean(
blueprint = blueprint,
new_data = new_data,
outcomes = outcomes
)
blueprint <- cleaned$blueprint
predictors <- cleaned$predictors
outcomes <- cleaned$outcomes
extras <- cleaned$extras
forge_recipe_default_process(
blueprint = blueprint,
predictors = predictors,
outcomes = outcomes,
extras = extras
)
}
# ------------------------------------------------------------------------------
forge_recipe_default_clean <- function(blueprint, new_data, outcomes) {
check_data_frame_or_matrix(new_data)
new_data <- coerce_to_tibble(new_data)
check_unique_column_names(new_data)
check_bool(outcomes)
predictors <- shrink(new_data, blueprint$ptypes$predictors)
predictors <- scream(
predictors,
blueprint$ptypes$predictors,
allow_novel_levels = blueprint$allow_novel_levels
)
if (outcomes) {
outcomes <- shrink(new_data, blueprint$ptypes$outcomes)
# Never allow novel levels for outcomes
outcomes <- scream(outcomes, blueprint$ptypes$outcomes)
} else {
outcomes <- NULL
}
extras <- forge_recipe_default_clean_extras(blueprint, new_data)
new_forge_clean(blueprint, predictors, outcomes, extras)
}
forge_recipe_default_clean_extras <- function(blueprint, new_data) {
if (is.null(blueprint$extra_role_ptypes)) {
extras <- list(roles = NULL)
return(extras)
}
extra_role_cols <- map(
blueprint$extra_role_ptypes,
shrink,
data = new_data
)
extra_role_cols <- map2(
extra_role_cols,
blueprint$extra_role_ptypes,
scream,
allow_novel_levels = blueprint$allow_novel_levels
)
extras <- list(roles = extra_role_cols)
extras
}
# ------------------------------------------------------------------------------
forge_recipe_default_process <- function(blueprint, predictors, outcomes, extras) {
rec <- blueprint$recipe
vars <- rec$term_info$variable
roles <- rec$term_info$role
roles <- chr_explicit_na(roles)
# Minimal name repair in case a predictor has multiple roles
# We just want to include it once, but without any name repair
new_data <- vec_cbind(
predictors,
outcomes,
!!!unname(extras$roles),
.name_repair = "minimal"
)
new_data_names <- names(new_data)
unique_names <- unique(new_data_names)
new_data <- new_data[unique_names]
# Can't move this inside core functions
# predictors and outcomes both must be present
baked_data <- recipes::bake(
object = rec,
new_data = new_data
)
processed_predictor_names <- vars[roles == "predictor"]
predictors <- baked_data[processed_predictor_names]
if (!is.null(outcomes)) {
processed_outcome_names <- vars[roles == "outcome"]
outcomes <- baked_data[processed_outcome_names]
}
processed <- forge_recipe_default_process_predictors(
blueprint = blueprint,
predictors = predictors
)
blueprint <- processed$blueprint
predictors <- processed$data
predictors_extras <- processed$extras
processed <- forge_recipe_default_process_outcomes(
blueprint = blueprint,
outcomes = outcomes
)
blueprint <- processed$blueprint
outcomes <- processed$data
outcomes_extras <- processed$extras
extras <- forge_recipe_default_process_extras(
extras,
rec,
baked_data,
predictors_extras,
outcomes_extras
)
new_forge_process(predictors, outcomes, extras)
}
forge_recipe_default_process_predictors <- function(blueprint, predictors) {
predictors <- maybe_add_intercept_column(predictors, blueprint$intercept)
predictors <- recompose(predictors, composition = blueprint$composition)
new_forge_process_terms(
blueprint = blueprint,
data = predictors
)
}
forge_recipe_default_process_outcomes <- function(blueprint, outcomes) {
# no outcomes to process
if (is.null(outcomes)) {
result <- new_forge_process_terms(
blueprint = blueprint,
data = outcomes
)
return(result)
}
new_forge_process_terms(
blueprint = blueprint,
data = outcomes
)
}
forge_recipe_default_process_extras <- function(extras,
rec,
baked_data,
predictors_extras,
outcomes_extras) {
# Remove old roles slot
extras$roles <- NULL
# Get the processed extra role columns after `bake()` has been called.
processed_extra_role_cols <- get_extra_role_columns_processed(
rec,
baked_data
)
extras <- c(
extras,
list(roles = processed_extra_role_cols),
new_extras(predictors_extras, outcomes_extras)
)
extras
}
# ------------------------------------------------------------------------------
get_original_predictor_ptype <- function(rec, data) {
roles <- rec$var_info$role
roles <- chr_explicit_na(roles)
original_names <- rec$var_info$variable[roles == "predictor"]
original_names <- original_names[!is.na(original_names)]
data <- data[original_names]
extract_ptype(data)
}
get_original_outcome_ptype <- function(rec, data) {
roles <- rec$var_info$role
roles <- chr_explicit_na(roles)
original_names <- rec$var_info$variable[roles == "outcome"]
data <- data[original_names]
extract_ptype(data)
}
get_extra_role_columns_original <- function(rec, data) {
# Extra roles that existed before `prep()` has been called.
# To get "extra" roles that are required at bake time:
# - Compute the bake role requirements named logical vector.
# It has information about every role in the original data.
# - Subset that vector to only `TRUE` locations, where the role is required
# - Remove the `"predictor"` role (it is always required, but isn't "extra")
info_type <- "var_info"
requirements <- compute_bake_role_requirements(rec)
# Filter down to the roles that are actually required
requirements <- requirements[requirements]
requirement_roles <- names(requirements)
extra_roles <- setdiff(requirement_roles, "predictor")
get_extra_role_columns(rec, data, extra_roles, info_type)
}
get_extra_role_columns_processed <- function(rec, data) {
# Extra roles that exist after baking either the training data or testing
# data (i.e. through `prep()` or `bake()`). This might include more or less
# roles than in the original data, because steps may have created or removed
# them along the way.
info_type <- "term_info"
data_roles <- rec[[info_type]][["role"]]
data_roles <- chr_explicit_na(data_roles)
extra_roles <- setdiff(data_roles, c("outcome", "predictor"))
get_extra_role_columns(rec, data, extra_roles, info_type)
}
get_extra_role_columns <- function(rec, data, extra_roles, info_type) {
has_any_extra_roles <- length(extra_roles) > 0
if (!has_any_extra_roles) {
return(NULL)
}
data_names <- colnames(data)
recipe_names <- rec[[info_type]][["variable"]]
recipe_roles <- rec[[info_type]][["role"]]
recipe_roles <- chr_explicit_na(recipe_roles)
out <- lapply(extra_roles, function(role) {
role_names <- recipe_names[recipe_roles == role]
# Must restrict to names that are actually in the `data` in case some
# roles were declared as "not required" and columns with those roles weren't
# passed to `forge()` through `new_data`.
role_names <- intersect(role_names, data_names)
data[role_names]
})
names(out) <- extra_roles
out
}
# ------------------------------------------------------------------------------
new_role_requirements <- function() {
# recipes:::new_role_requirements()
list(
bake = new_bake_role_requirements()
)
}
get_role_requirements <- function(recipe) {
# recipes:::get_role_requirements()
recipe$requirements %||% new_role_requirements()
}
new_bake_role_requirements <- function() {
# recipes:::new_bake_role_requirements()
set_names(logical(), nms = character())
}
get_bake_role_requirements <- function(recipe) {
# recipes:::get_bake_role_requirements()
requirements <- get_role_requirements(recipe)
requirements$bake
}
default_bake_role_requirements <- function() {
# recipes:::default_bake_role_requirements()
c(
"outcome" = FALSE,
"predictor" = TRUE,
"case_weights" = FALSE,
"NA" = TRUE
)
}
compute_bake_role_requirements <- function(recipe) {
# recipes:::compute_bake_role_requirements()
var_info <- recipe$var_info
var_roles <- var_info$role
var_roles <- chr_explicit_na(var_roles)
var_roles <- unique(var_roles)
# Start with default requirements
requirements <- default_bake_role_requirements()
# Drop unused default requirements
requirements <- requirements[names(requirements) %in% var_roles]
# Update with nonstandard roles in the recipe, which are required by default
nonstandard_roles <- var_roles[!var_roles %in% names(requirements)]
requirements[nonstandard_roles] <- TRUE
# Override with `update_role_requirements()` changes
user_requirements <- get_bake_role_requirements(recipe)
requirements[names(user_requirements)] <- user_requirements
requirements
}
chr_explicit_na <- function(x) {
# recipes:::chr_explicit_na()
# To turn `NA_character_` into `"NA"` because you can't match
# against `NA_character_` when assigning with `[<-`
x[is.na(x)] <- "NA"
x
}
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