rename validate() to validate_forecast()

NAMESPACE

@@ -8,10 +8,10 @@ S3method(score,scoringutils_binary)
 S3method(score,scoringutils_point)
 S3method(score,scoringutils_quantile)
 S3method(score,scoringutils_sample)
-S3method(validate,scoringutils_binary)
-S3method(validate,scoringutils_point)
-S3method(validate,scoringutils_quantile)
-S3method(validate,scoringutils_sample)
+S3method(validate_forecast,scoringutils_binary)
+S3method(validate_forecast,scoringutils_point)
+S3method(validate_forecast,scoringutils_quantile)
+S3method(validate_forecast,scoringutils_sample)
 export(abs_error)
 export(add_coverage)
 export(add_pairwise_comparison)
@@ -69,7 +69,7 @@ export(summarize_scores)
 export(theme_scoringutils)
 export(transform_forecasts)
 export(underprediction)
-export(validate)
+export(validate_forecast)
 export(validate_general)
 export(wis)
 importFrom(Metrics,ae)

NEWS.md

@@ -20,7 +20,7 @@ The update introduces breaking changes. If you want to keep using the older vers
     - `observed`: numeric, either a scalar or a vector
     - `predicted`: numeric, a vector (if `observed` is a scalar) or a matrix (if `observed` is a vector)
     - `quantile`: numeric, a vector with quantile-levels. Can alternatively be a matrix of the same shape as `predicted`.
-- `check_forecasts()` was replaced by a different workflow. There now is a function, `as_forecast()`, that determines forecast type of the data, constructs a forecasting object and validates it using the function `validate()` (a generic that dispatches the correct method based on the forecast type). Objects of class `forecast_binary`, `forecast_point`, `forecast_sample` and `forecast_quantile` have print methods that fulfill the functionality of `check_forecasts()`.
+- `check_forecasts()` was replaced by a different workflow. There now is a function, `as_forecast()`, that determines forecast type of the data, constructs a forecasting object and validates it using the function `validate_forecast()` (a generic that dispatches the correct method based on the forecast type). Objects of class `forecast_binary`, `forecast_point`, `forecast_sample` and `forecast_quantile` have print methods that fulfill the functionality of `check_forecasts()`.
 - The functionality for computing pairwise comparisons was now split from `summarise_scores()`. Instead of doing pairwise comparisons as part of summarising scores, a new function, `add_pairwise_comparison()`, was introduced that takes summarised scores as an input and adds pairwise comparisons to it. 
 - `add_coverage()` was reworked completely. It's new purpose is now to add coverage information to the raw forecast data (essentially fulfilling some of the functionality that was previously covered by `score_quantile()`)
 - Support for the interval format was mostly dropped (see PR #525 by @nikosbosse and reviewed by @seabbs)

R/get_-functions.R

@@ -151,7 +151,7 @@ get_metrics <- function(scores) {
 #' the columns that are protected, i.e. those returned by
 #' [get_protected_columns()] as well as the names of the metrics that were
 #' specified during scoring, if any.
-#' @inheritParams validate
+#' @inheritParams validate_forecast
 #' @param check_conflict Whether or not to check whether there is a conflict
 #' between a stored attribute and the inferred forecast unit. When you create
 #' a forecast object, the forecast unit is stored as an attribute. If you
@@ -183,7 +183,7 @@ get_forecast_unit <- function(data, check_conflict = FALSE) {
 #' @description Helper function to get the names of all columns in a data frame
 #' that are protected columns.
 #'
-#' @inheritParams validate
+#' @inheritParams validate_forecast
 #'
 #' @return A character vector with the names of protected columns in the data.
 #' If data is `NULL` (default) then it returns a list of all columns that are

R/score.R

@@ -75,7 +75,7 @@ score.default <- function(data, ...) {
 #' @rdname score
 #' @export
 score.scoringutils_binary <- function(data, metrics = metrics_binary, ...) {
-  data <- validate(data)
+  data <- validate_forecast(data)
   data <- remove_na_observed_predicted(data)
   metrics <- validate_metrics(metrics)
 
@@ -95,7 +95,7 @@ score.scoringutils_binary <- function(data, metrics = metrics_binary, ...) {
 #' @rdname score
 #' @export
 score.scoringutils_point <- function(data, metrics = metrics_point, ...) {
-  data <- validate(data)
+  data <- validate_forecast(data)
   data <- remove_na_observed_predicted(data)
   metrics <- validate_metrics(metrics)
 
@@ -112,7 +112,7 @@ score.scoringutils_point <- function(data, metrics = metrics_point, ...) {
 #' @rdname score
 #' @export
 score.scoringutils_sample <- function(data, metrics = metrics_sample, ...) {
-  data <- validate(data)
+  data <- validate_forecast(data)
   data <- remove_na_observed_predicted(data)
   forecast_unit <- attr(data, "forecast_unit")
   metrics <- validate_metrics(metrics)
@@ -149,7 +149,7 @@ score.scoringutils_sample <- function(data, metrics = metrics_sample, ...) {
 #' @rdname score
 #' @export
 score.scoringutils_quantile <- function(data, metrics = metrics_quantile, ...) {
-  data <- validate(data)
+  data <- validate_forecast(data)
   data <- remove_na_observed_predicted(data)
   forecast_unit <- attr(data, "forecast_unit")
   metrics <- validate_metrics(metrics)

R/validate.R

@@ -29,7 +29,7 @@ as_forecast <- function(data) {
   data <- new_scoringutils(data, paste0("scoringutils_", forecast_type))
 
   # validate class
-  validate(data)
+  validate_forecast(data)
 }
 
 
@@ -48,15 +48,18 @@ as_forecast <- function(data) {
 #' @importFrom checkmate assert_data_frame
 #' @export
 #' @keywords check-forecasts
-validate <- function(data, ...) {
-  UseMethod("validate")
+#' @examples
+#' forecast <- as_forecast(example_binary)
+#' validate_forecast(forecast)
+validate_forecast <- function(data, ...) {
+  UseMethod("validate_forecast")
 }
 
 
 #' @rdname validate
 #' @export
 #' @keywords check-forecasts
-validate.scoringutils_binary <- function(data, ...) {
+validate_forecast.scoringutils_binary <- function(data, ...) {
   data <- validate_general(data)
 
   columns_correct <- test_columns_not_present(data, c("sample_id", "quantile"))
@@ -77,7 +80,7 @@ validate.scoringutils_binary <- function(data, ...) {
 #' @rdname validate
 #' @export
 #' @keywords check-forecasts
-validate.scoringutils_point <- function(data, ...) {
+validate_forecast.scoringutils_point <- function(data, ...) {
   data <- validate_general(data)
 
   input_check <- check_input_point(data$observed, data$predicted)
@@ -91,7 +94,7 @@ validate.scoringutils_point <- function(data, ...) {
 
 #' @rdname validate
 #' @export
-validate.scoringutils_quantile <- function(data, ...) {
+validate_forecast.scoringutils_quantile <- function(data, ...) {
   data <- validate_general(data)
   assert_numeric(data$quantile, lower = 0, upper = 1)
   return(data[])
@@ -100,7 +103,7 @@ validate.scoringutils_quantile <- function(data, ...) {
 #' @rdname validate
 #' @export
 #' @keywords check-forecasts
-validate.scoringutils_sample <- function(data, ...) {
+validate_forecast.scoringutils_sample <- function(data, ...) {
   data <- validate_general(data)
   return(data[])
 }

README.Rmd

@@ -85,12 +85,12 @@ example_quantile %>%
 
 ### Scoring forecasts
 
-Forecasts can be easily and quickly scored using the `score()` function. `score()` automatically tries to determine the `forecast_unit`, i.e. the set of columns that uniquely defines a single forecast, by taking all column names of the data into account. However, it is recommended to set the forecast unit manually using `set_forecast_unit()` as this may help to avoid errors, especially when scoringutils is used in automated pipelines. The function `set_forecast_unit()` will simply drop unneeded columns. To verify everything is in order, the function `validate()` should be used. The result of that check can then passed directly into `score()`. `score()` returns unsummarised scores, which in most cases is not what the user wants. Here we make use of additional functions from `scoringutils` to add empirical coverage-levels (`add_coverage()`), and scores relative to a baseline model (here chosen to be the EuroCOVIDhub-ensemble model). See the getting started vignette for more details. Finally we summarise these scores by model and target type.
+Forecasts can be easily and quickly scored using the `score()` function. `score()` automatically tries to determine the `forecast_unit`, i.e. the set of columns that uniquely defines a single forecast, by taking all column names of the data into account. However, it is recommended to set the forecast unit manually using `set_forecast_unit()` as this may help to avoid errors, especially when scoringutils is used in automated pipelines. The function `set_forecast_unit()` will simply drop unneeded columns. To verify everything is in order, the function `validate_forecast()` should be used. The result of that check can then passed directly into `score()`. `score()` returns unsummarised scores, which in most cases is not what the user wants. Here we make use of additional functions from `scoringutils` to add empirical coverage-levels (`add_coverage()`), and scores relative to a baseline model (here chosen to be the EuroCOVIDhub-ensemble model). See the getting started vignette for more details. Finally we summarise these scores by model and target type.
 
 ```{r score-example}
 example_quantile %>%
   set_forecast_unit(c("location", "target_end_date", "target_type", "horizon", "model")) %>%
-  validate() %>%
+  validate_forecast() %>%
   add_coverage() %>%
   score() %>%
   summarise_scores(

README.md

@@ -116,7 +116,7 @@ column names of the data into account. However, it is recommended to set
 the forecast unit manually using `set_forecast_unit()` as this may help
 to avoid errors, especially when scoringutils is used in automated
 pipelines. The function `set_forecast_unit()` will simply drop unneeded
-columns. To verify everything is in order, the function `validate()`
+columns. To verify everything is in order, the function `validate_forecast()`
 should be used. The result of that check can then passed directly into
 `score()`. `score()` returns unsummarised scores, which in most cases is
 not what the user wants. Here we make use of additional functions from
@@ -128,7 +128,7 @@ details. Finally we summarise these scores by model and target type.
 ``` r
 example_quantile %>%
   set_forecast_unit(c("location", "target_end_date", "target_type", "horizon", "model")) %>%
-  validate() %>%
+  validate_forecast() %>%
   add_coverage() %>%
   score() %>%
   summarise_scores(

tests/testthat/test-utils.R

@@ -104,7 +104,7 @@ test_that("run_safely() works as expected", {
 # })
 
 # test_that("is_scoringutils_check() is working", {
-#   checked <- suppressMessages(validate(example_binary))
+#   checked <- suppressMessages(validate_forecast(example_binary))
 #   expect_true(is_scoringutils_check(checked))
 #
 #   checked$cleaned_data <- NULL