Issue #520: Rename forecast classes from scoringutils_* to forecast_*

NAMESPACE

@@ -5,14 +5,14 @@ S3method(print,scoringutils_check)
 S3method(quantile_to_interval,data.frame)
 S3method(quantile_to_interval,numeric)
 S3method(score,default)
-S3method(score,scoringutils_binary)
-S3method(score,scoringutils_point)
-S3method(score,scoringutils_quantile)
-S3method(score,scoringutils_sample)
-S3method(validate_forecast,scoringutils_binary)
-S3method(validate_forecast,scoringutils_point)
-S3method(validate_forecast,scoringutils_quantile)
-S3method(validate_forecast,scoringutils_sample)
+S3method(score,forecast_binary)
+S3method(score,forecast_point)
+S3method(score,forecast_quantile)
+S3method(score,forecast_sample)
+S3method(validate_forecast,forecast_binary)
+S3method(validate_forecast,forecast_point)
+S3method(validate_forecast,forecast_quantile)
+S3method(validate_forecast,forecast_sample)
 export(abs_error)
 export(add_coverage)
 export(add_pairwise_comparison)
@@ -41,7 +41,7 @@ export(mad_sample)
 export(make_NA)
 export(make_na)
 export(merge_pred_and_obs)
-export(new_scoringutils)
+export(new_forecast)
 export(overprediction)
 export(pairwise_comparison)
 export(pit)

NEWS.md

@@ -6,6 +6,7 @@ The update introduces breaking changes. If you want to keep using the older vers
 
 ## Package updates
 - In `score()`, required columns "true_value" and "prediction" were renamed and replaced by required columns "observed" and "predicted". Scoring functions now also use the function arguments "observed" and "predicted" everywhere consistently. 
+- The overall scoring workflow was updated. `score()` is now a generic function that dispatches the correct method based on the forecast type. forecast types currently supported are "binary", "point", "sample" and "quantile" with corresponding classes "forecast_binary", "forecast_point", "forecast_sample" and "forecast_quantile". An object of class `forecast_*` can be created using the function `as_forecast()`, which also replaces the previous function `check_forecasts()` (see more information below). 
 - Scoring functions received a consistent interface and input checks:
   - metrics for binary forecasts:
     - `observed`: factor with exactly 2 levels

R/get_-functions.R

@@ -250,7 +250,7 @@ get_duplicate_forecasts <- function(data, forecast_unit = NULL) {
 
 #' @title Get a list of all attributes of a scoringutils object
 #'
-#' @param object A object of class `scoringutils_`
+#' @param object A object of class `forecast_`
 #'
 #' @return A named list with the attributes of that object.
 #' @keywords internal

R/score.R

@@ -70,13 +70,13 @@ score <- function(data, ...) {
 score.default <- function(data, ...) {
   assert(check_data_columns(data))
   forecast_type <- get_forecast_type(data)
-  data <- new_scoringutils(data, paste0("scoringutils_", forecast_type))
+  data <- new_forecast(data, paste0("forecast_", forecast_type))
   score(data, ...)
 }
 
 #' @rdname score
 #' @export
-score.scoringutils_binary <- function(data, metrics = metrics_binary, ...) {
+score.forecast_binary <- function(data, metrics = metrics_binary, ...) {
   data <- validate_forecast(data)
   data <- remove_na_observed_predicted(data)
   metrics <- validate_metrics(metrics)
@@ -96,7 +96,7 @@ score.scoringutils_binary <- function(data, metrics = metrics_binary, ...) {
 #' @importFrom Metrics se ae ape
 #' @rdname score
 #' @export
-score.scoringutils_point <- function(data, metrics = metrics_point, ...) {
+score.forecast_point <- function(data, metrics = metrics_point, ...) {
   data <- validate_forecast(data)
   data <- remove_na_observed_predicted(data)
   metrics <- validate_metrics(metrics)
@@ -113,7 +113,7 @@ score.scoringutils_point <- function(data, metrics = metrics_point, ...) {
 
 #' @rdname score
 #' @export
-score.scoringutils_sample <- function(data, metrics = metrics_sample, ...) {
+score.forecast_sample <- function(data, metrics = metrics_sample, ...) {
   data <- validate_forecast(data)
   data <- remove_na_observed_predicted(data)
   forecast_unit <- attr(data, "forecast_unit")
@@ -150,7 +150,7 @@ score.scoringutils_sample <- function(data, metrics = metrics_sample, ...) {
 #' @importFrom data.table `:=` as.data.table rbindlist %like%
 #' @rdname score
 #' @export
-score.scoringutils_quantile <- function(data, metrics = metrics_quantile, ...) {
+score.forecast_quantile <- function(data, metrics = metrics_quantile, ...) {
   data <- validate_forecast(data)
   data <- remove_na_observed_predicted(data)
   forecast_unit <- attr(data, "forecast_unit")
@@ -176,7 +176,9 @@ score.scoringutils_quantile <- function(data, metrics = metrics_quantile, ...) {
     observed <- data$observed
     predicted <- do.call(rbind, data$predicted)
     quantile <- unlist(unique(data$quantile))
-    data[, c("observed", "predicted", "quantile", "scoringutils_quantile") := NULL]
+    data[, c(
+      "observed", "predicted", "quantile", "scoringutils_quantile"
+    ) := NULL]
 
     data <- apply_metrics(
       data, metrics,

R/validate.R

@@ -6,14 +6,14 @@
 #' quantile-based) from the input data (using the function
 #' [get_forecast_type()]. It then constructs an object of the
 #' appropriate class (`forecast_binary`, `forecast_point`, `forecast_sample`, or
-#' `forecast_quantile`, using the function [new_scoringutils()]).
+#' `forecast_quantile`, using the function [new_forecast()]).
 #' Lastly, it calls [as_forecast()] on the object to make sure it conforms with
 #' the required input formats.
 #' @inheritParams score
 #' @inheritSection forecast_types Forecast types and input format
 #' @return Depending on the forecast type, an object of class
-#' `scoringutils_binary`, `scoringutils_point`, `scoringutils_sample` or
-#' `scoringutils_quantile`.
+#' `forecast_binary`, `forecast_point`, `forecast_sample` or
+#' `forecast_quantile`.
 #' @export
 #' @keywords check-forecasts
 #' @examples
@@ -32,7 +32,7 @@ as_forecast.default <- function(data, ...) {
   forecast_type <- get_forecast_type(data)
 
   # construct class
-  data <- new_scoringutils(data, paste0("scoringutils_", forecast_type))
+  data <- new_forecast(data, paste0("forecast_", forecast_type))
 
   # validate class
   validate_forecast(data)
@@ -48,8 +48,8 @@ as_forecast.default <- function(data, ...) {
 #' @inheritParams score
 #' @inheritSection forecast_types Forecast types and input format
 #' @return Depending on the forecast type, an object of class
-#' `scoringutils_binary`, `scoringutils_point`, `scoringutils_sample` or
-#' `scoringutils_quantile`.
+#' `forecast_binary`, `forecast_point`, `forecast_sample` or
+#' `forecast_quantile`.
 #' @importFrom data.table ':=' is.data.table
 #' @importFrom checkmate assert_data_frame
 #' @export
@@ -62,10 +62,9 @@ validate_forecast <- function(data, ...) {
 }
 
 
-#' @rdname validate
 #' @export
 #' @keywords check-forecasts
-validate_forecast.scoringutils_binary <- function(data, ...) {
+validate_forecast.forecast_binary <- function(data, ...) {
   data <- validate_general(data)
 
   columns_correct <- test_columns_not_present(data, c("sample_id", "quantile"))
@@ -83,10 +82,10 @@ validate_forecast.scoringutils_binary <- function(data, ...) {
   return(data[])
 }
 
-#' @rdname validate
+
 #' @export
 #' @keywords check-forecasts
-validate_forecast.scoringutils_point <- function(data, ...) {
+validate_forecast.forecast_point <- function(data, ...) {
   data <- validate_general(data)
 
   input_check <- check_input_point(data$observed, data$predicted)
@@ -98,22 +97,24 @@ validate_forecast.scoringutils_point <- function(data, ...) {
   return(data[])
 }
 
-#' @rdname validate
+
 #' @export
-validate_forecast.scoringutils_quantile <- function(data, ...) {
+#' @keywords check-forecasts
+validate_forecast.forecast_quantile <- function(data, ...) {
   data <- validate_general(data)
   assert_numeric(data$quantile, lower = 0, upper = 1)
   return(data[])
 }
 
-#' @rdname validate
+
 #' @export
 #' @keywords check-forecasts
-validate_forecast.scoringutils_sample <- function(data, ...) {
+validate_forecast.forecast_sample <- function(data, ...) {
   data <- validate_general(data)
   return(data[])
 }
 
+
 #' @title Apply scoringutls input checks that are the same across forecast types
 #'
 #' @description
@@ -181,7 +182,7 @@ validate_general <- function(data) {
 #' @return An object of the class indicated by `classname`
 #' @export
 #' @keywords internal
-new_scoringutils <- function(data, classname) {
+new_forecast <- function(data, classname) {
   data <- as.data.table(data)
   data <- assure_model_column(data)
   class(data) <- c(classname, class(data))

tests/testthat/test-check_forecasts.R

@@ -1,6 +1,6 @@
 test_that("as_forecast() function works", {
   check <- suppressMessages(as_forecast(example_quantile))
-  expect_s3_class(check, "scoringutils_quantile")
+  expect_s3_class(check, "forecast_quantile")
 })
 
 test_that("as_forecast() function has an error for empty data.frame", {

tests/testthat/test-metrics-point.R

@@ -179,9 +179,9 @@ test_that("abs error is correct, point and median forecasts same", {
     observations = truth_scoringutils
   )
 
-  data_scoringutils_point <- data_scoringutils[type == "point"][, quantile := NULL]
+  data_forecast_point <- data_scoringutils[type == "point"][, quantile := NULL]
 
-  eval <- score(data = data_scoringutils_point)
+  eval <- score(data = data_forecast_point)
   eval <- summarise_scores(eval,
     by = c(
       "location", "target_end_date",

tests/testthat/test-score.R

@@ -38,7 +38,7 @@ test_that("function produces output for a binary case", {
   expect_true("brier_score" %in% names(eval))
 })
 
-test_that("score.scoringutils_binary() errors with only NA values", {
+test_that("score.forecast_binary() errors with only NA values", {
   only_nas <- copy(example_binary)[, predicted := NA_real_]
   expect_error(
     score(only_nas),
@@ -156,7 +156,7 @@ test_that("Changing metrics names works", {
 })
 
 
-test_that("score.scoringutils_point() errors with only NA values", {
+test_that("score.forecast_point() errors with only NA values", {
   only_nas <- copy(example_point)[, predicted := NA_real_]
   expect_error(
     score(only_nas),
@@ -239,7 +239,7 @@ test_that("WIS is the same with other metrics omitted or included", {
 })
 
 
-test_that("score.scoringutils_quantile() errors with only NA values", {
+test_that("score.forecast_quantile() errors with only NA values", {
   only_nas <- copy(example_quantile)[, predicted := NA_real_]
   expect_error(
     score(only_nas),