Rename apply_metrics() to apply_rules()

R/score.R

@@ -81,7 +81,7 @@ score.scoringutils_binary <- function(data, metrics = metrics_binary, ...) {
   data <- remove_na_observed_predicted(data)
   metrics <- validate_metrics(metrics)
 
-  data <- apply_metrics(
+  data <- apply_rules(
     data, metrics,
     data$observed, data$predicted, ...
   )
@@ -101,7 +101,7 @@ score.scoringutils_point <- function(data, metrics = metrics_point, ...) {
   data <- remove_na_observed_predicted(data)
   metrics <- validate_metrics(metrics)
 
-  data <- apply_metrics(
+  data <- apply_rules(
     data, metrics,
     data$observed, data$predicted, ...
   )
@@ -135,7 +135,7 @@ score.scoringutils_sample <- function(data, metrics = metrics_sample, ...) {
     predicted <- do.call(rbind, data$predicted)
     data[, c("observed", "predicted", "scoringutils_N") := NULL]
 
-    data <- apply_metrics(
+    data <- apply_rules(
       data, metrics,
       observed, predicted, ...
     )
@@ -178,7 +178,7 @@ score.scoringutils_quantile <- function(data, metrics = metrics_quantile, ...) {
     quantile <- unlist(unique(data$quantile))
     data[, c("observed", "predicted", "quantile", "scoringutils_quantile") := NULL]
 
-    data <- apply_metrics(
+    data <- apply_rules(
       data, metrics,
       observed, predicted, quantile, ...
     )
@@ -194,14 +194,15 @@ score.scoringutils_quantile <- function(data, metrics = metrics_quantile, ...) {
 
 #' @title Apply A List Of Functions To A Data Table Of Forecasts
 #' @description This helper function applies scoring rules (stored as a list of
-#' functions) to a data table of. `apply_metrics` is used within `score()` to
-#' apply all scoring rules to the data.
+#' functions) to a data table of forecasts. `apply_rules` is used within
+#' `score()` to apply all scoring rules to the data.
 #' Scoring rules are wrapped in [run_safely()] to catch errors and to make
 #' sure that only arguments are passed to the scoring rule that are actually
 #' accepted by it.
 #' @inheritParams score
 #' @return A data table with the forecasts and the calculated metrics
-apply_metrics <- function(data, metrics, ...) {
+#' @keywords internal
+apply_rules <- function(data, metrics, ...) {
   expr <- expression(
     data[, (metric_name) := do.call(run_safely, list(..., fun = fun))]
   )

tests/testthat/test-score.R

@@ -278,29 +278,29 @@ test_that("function throws an error if data is missing", {
 })
 
 # =============================================================================
-# `apply_metrics()`
+# `apply_rules()`
 # =============================================================================
 
-test_that("apply_metrics() works", {
+test_that("apply_rules() works", {
 
   dt <- data.table::data.table(x = 1:10)
-  scoringutils:::apply_metrics(
+  scoringutils:::apply_rules(
     data = dt, metrics = list("test" = function(x) x + 1),
     dt$x
   )
   expect_equal(dt$test, 2:11)
 
   # additional named argument works
   expect_no_condition(
-    scoringutils:::apply_metrics(
+    scoringutils:::apply_rules(
       data = dt, metrics = list("test" = function(x) x + 1),
       dt$x, y = dt$test)
   )
 
   # additional unnamed argument does not work
 
   expect_warning(
-    scoringutils:::apply_metrics(
+    scoringutils:::apply_rules(
       data = dt, metrics = list("test" = function(x) x + 1),
       dt$x, dt$test)
   )