From cc8cc9d17e24c01dd020dddb6e7bd77ac7e6b8e1 Mon Sep 17 00:00:00 2001
From: nikosbosse <nikosbosse@gmail.com>
Date: Tue, 24 Sep 2024 22:17:00 +0200
Subject: [PATCH] delete `validate_forecast()`

---
 NAMESPACE                      |  1 -
 NEWS.md                        |  2 +-
 R/forecast.R                   | 15 ---------------
 man/validate_forecast.Rd       | 33 ---------------------------------
 tests/testthat/test-forecast.R | 18 ------------------
 5 files changed, 1 insertion(+), 68 deletions(-)
 delete mode 100644 man/validate_forecast.Rd

diff --git a/NAMESPACE b/NAMESPACE
index e24968a9..ece27fce 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -92,7 +92,6 @@ export(theme_scoringutils)
 export(transform_forecasts)
 export(underprediction_quantile)
 export(underprediction_sample)
-export(validate_forecast)
 export(wis)
 importFrom(Metrics,ae)
 importFrom(Metrics,ape)
diff --git a/NEWS.md b/NEWS.md
index 806b047b..824c9195 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -50,7 +50,7 @@ of our [original](https://doi.org/10.48550/arXiv.2205.07090) `scoringutils` pape
     - The function `interval_score()` was made an internal function rather than being exported to users. We recommend using `wis()` instead. 
 
 ### (Re-)Validating forecast objects
-- To create and validate a new `forecast` object, users can use `as_forecast_<type>()`. To revalidate an existing `forecast` object users can call `assert_forecast()` (which validates the input and returns `invisible(NULL)`. `assert_forecast()` is a generic with methods for the different forecast types. Alternatively, `validate_forecast()` can be used (which calls `assert_forecast()`), which returns the input and is useful in a pipe. Lastly, users can simply print the object to obtain additional information. 
+- To create and validate a new `forecast` object, users can use `as_forecast_<type>()`. To revalidate an existing `forecast` object users can call `assert_forecast()` (which validates the input and returns `invisible(NULL)`. `assert_forecast()` is a generic with methods for the different forecast types. Alternatively, users can call ``as_forecast_<type>()` again to re-validate a forecast object. Simply printing the object will also provide some additional information. 
 - Users can test whether an object is of class `forecast_*()` using the function `is_forecast()`. Users can also test for a specific `forecast_*` class using the appropriate `is_forecast.forecast_*` method. For example, to check whether an object is of class `forecast_quantile`, you would use you would use `scoringutils:::is_forecast.forecast_quantile()`.
 
 ### Pairwise comparisons and relative skill
diff --git a/R/forecast.R b/R/forecast.R
index 4165d2d7..efbcb6df 100644
--- a/R/forecast.R
+++ b/R/forecast.R
@@ -502,21 +502,6 @@ assert_forecast.forecast_nominal <- function(
 }
 
 
-#' @title Re-validate an existing forecast object
-#'
-#' @description
-#' The function re-validates an existing forecast object. It is similar to
-#' [assert_forecast()], but returns the input data instead of an invisible
-#' `NULL`. See [as_forecast()] for details on the expected input formats.
-#' @inherit assert_forecast params return examples
-#' @export
-#' @keywords validate-forecast-object
-validate_forecast <- function(forecast, forecast_type = NULL, verbose = TRUE) {
-  assert_forecast(forecast, forecast_type, verbose)
-  return(forecast)
-}
-
-
 #' @title Validation common to all forecast types
 #'
 #' @description
diff --git a/man/validate_forecast.Rd b/man/validate_forecast.Rd
deleted file mode 100644
index 8b24513d..00000000
--- a/man/validate_forecast.Rd
+++ /dev/null
@@ -1,33 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/forecast.R
-\name{validate_forecast}
-\alias{validate_forecast}
-\title{Re-validate an existing forecast object}
-\usage{
-validate_forecast(forecast, forecast_type = NULL, verbose = TRUE)
-}
-\arguments{
-\item{forecast}{A forecast object (a validated data.table with predicted and
-observed values, see \code{\link[=as_forecast]{as_forecast()}}).}
-
-\item{forecast_type}{(optional) The forecast type you expect the forecasts
-to have. If the forecast type as determined by \code{scoringutils} based on the
-input does not match this, an error will be thrown. If \code{NULL} (the
-default), the forecast type will be inferred from the data.}
-
-\item{verbose}{Logical. If \code{FALSE} (default is \code{TRUE}), no messages and
-warnings will be created.}
-}
-\value{
-Returns \code{NULL} invisibly.
-}
-\description{
-The function re-validates an existing forecast object. It is similar to
-\code{\link[=assert_forecast]{assert_forecast()}}, but returns the input data instead of an invisible
-\code{NULL}. See \code{\link[=as_forecast]{as_forecast()}} for details on the expected input formats.
-}
-\examples{
-forecast <- as_forecast_binary(example_binary)
-assert_forecast(forecast)
-}
-\keyword{validate-forecast-object}
diff --git a/tests/testthat/test-forecast.R b/tests/testthat/test-forecast.R
index 2ae49880..632a7cda 100644
--- a/tests/testthat/test-forecast.R
+++ b/tests/testthat/test-forecast.R
@@ -278,24 +278,6 @@ test_that("assert_forecast_generic() works as expected with a data.frame", {
 })
 
 
-# ==============================================================================
-# validate_forecast()
-# ==============================================================================
-
-test_that("validate_forecast() works as expected", {
-  # check that validate forecast returns itself
-  expect_no_condition(
-    out <- validate_forecast(as_forecast_point(na.omit(example_point)))
-  )
-  expect_true(!is.null(out))
-
-  expect_equal(
-    validate_forecast(as_forecast_point(na.omit(example_point))),
-    as_forecast_point(na.omit(example_point))
-  )
-})
-
-
 # ==============================================================================
 # new_forecast()
 # ==============================================================================