R/zzz-compat-vctrs-vfold_cv.R

In rsample: General Resampling Infrastructure

# This file was generated, do not edit by hand
# Please edit inst/generate_vctrs.R instead
# ------------------------------------------------------------------------------
# vfold_cv

# `vec_restore()`
#
# Called at the end of `vec_slice()` and `vec_ptype()` after all slicing has
# been done on the proxy object.
#
# If no changes have been made to the row/column structure of rset specific
# columns, then we can return an rset subclass. Otherwise, the resulting
# object is no longer guaranteed to return a valid rset, and we have to
# fallback to a bare tibble.
#
# It is very important that the result of `vec_ptype()` is a bare tibble.
# This ensures that the `vec_ptype2.<rset-subclass>.<rset-subclass>()` methods
# never get called. `vec_ptype()` is able to return a bare tibble because it
# essentially takes a 0-row slice of the rset, and then calls `vec_restore()`.
# Because the row structure has been modified, we return a bare tibble from
# `vec_restore.<rset-subclass>()`.
#
# Currently `vec_restore()` uses inheritance in vctrs, but I don't expect this
# to be the case in the future. For that reason, I use explicit methods for
# each individual rset subclass, rather than implementing `vec_restore.rset()`.
#' @export
vec_restore.vfold_cv <- function(x, to, ...) {
  rset_reconstruct(x, to)
}

# `vec_ptype2()`
#
# The purpose of `vec_ptype2()` methods is generally to determine the type
# of the output in operations like `vec_c()` or `vec_rbind()`. However, this
# implementation does not use any custom `vec_ptype2()` methods at all. This is
# explained below.
#
# `vec_ptype2()` internally works by calling `vec_ptype()` on both `x` and `y`,
# and then looking up the common type of those two ptypes. Generally, the
# ptype of a vector `x` returned from `vec_ptype()` has the same class as `x`.
# However, for rsample objects it makes more sense for the ptype of an rset to
# be a bare tibble. The reason for this is because a ptype of a data frame is
# generally a 0-row slice. However for rsample rsets this doesn't make sense
# (you can't have 0 rows in a 10-fold cv rset), so instead we return a bare
# tibble as the ptype.
# So when `vec_ptype2()` is called on an rset, that rset is downgraded to a
# bare tibble _before_ the search for a `vec_ptype2()` method occurs. This
# means that it will never find a ptype2 method such as
# `vec_ptype2.bootstraps.tbl_df()`, because the <bootstraps> will become a
# tbl_df first. This means that we rely entirely on the `tbl_df` ptype2 methods,
# which are already implemented in vctrs to return another tbl_df.
#
# The implications of this are that whenever a rset subclass is combined with
# another rset subclass or bare tibble, the resulting common type is always
# another bare tibble. So if you `vec_c(<bootstraps>, <tbl_df>)` the result
# will always be a tibble, never a bootstraps object. This makes sense because
# you might be adding rows, which would invalidate the structure of the
# bootstraps object.

#' @export
vec_ptype2.vfold_cv.vfold_cv <- function(x, y, ..., x_arg = "", y_arg = "") {
  stop_never_called("vec_ptype2.vfold_cv.vfold_cv")
}
#' @export
vec_ptype2.vfold_cv.tbl_df <- function(x, y, ..., x_arg = "", y_arg = "") {
  stop_never_called("vec_ptype2.vfold_cv.tbl_df")
}
#' @export
vec_ptype2.tbl_df.vfold_cv <- function(x, y, ..., x_arg = "", y_arg = "") {
  stop_never_called("vec_ptype2.tbl_df.vfold_cv")
}
#' @export
vec_ptype2.vfold_cv.data.frame <- function(x, y, ..., x_arg = "", y_arg = "") {
  stop_never_called("vec_ptype2.vfold_cv.data.frame")
}
#' @export
vec_ptype2.data.frame.vfold_cv <- function(x, y, ..., x_arg = "", y_arg = "") {
  stop_never_called("vec_ptype2.data.frame.vfold_cv")
}

# `vec_cast()`
#
# The `vec_cast()` methods for rset objects really only serve 1 purpose. They
# cast an rset subclass to a tibble or data frame. The cast to tibble is most
# useful. Most of the operations in vctrs work by finding a common type
# with `vec_ptype2()`, and then casting all of the inputs to that common type.
# Because `vec_ptype2()` returns a bare tibble anytime a rset-subclass is
# involved, we will always be casting the rset subclass to a tibble.
# The cast method uses `vctrs::tib_cast()`, which always returns a bare tibble
# with all of the data in `x` cast to the type of `to`.

#' @export
vec_cast.vfold_cv.vfold_cv <- function(x, to, ..., x_arg = "", to_arg = "") {
  stop_incompatible_cast_rset(x, to, x_arg = x_arg, to_arg = to_arg)
}
#' @export
vec_cast.vfold_cv.tbl_df <- function(x, to, ..., x_arg = "", to_arg = "") {
  stop_incompatible_cast_rset(x, to, x_arg = x_arg, to_arg = to_arg)
}
#' @export
vec_cast.tbl_df.vfold_cv <- function(x, to, ..., x_arg = "", to_arg = "") {
  tib_cast(x, to, ..., x_arg = x_arg, to_arg = to_arg)
}
#' @export
vec_cast.vfold_cv.data.frame <- function(x, to, ..., x_arg = "", to_arg = "") {
  stop_incompatible_cast_rset(x, to, x_arg = x_arg, to_arg = to_arg)
}
#' @export
vec_cast.data.frame.vfold_cv <- function(x, to, ..., x_arg = "", to_arg = "") {
  df_cast(x, to, ..., x_arg = x_arg, to_arg = to_arg)
}