Simplify score_model_out

.Rbuildignore

@@ -15,3 +15,4 @@
 ^\.Rdata$
 ^\.httr-oauth$
 ^\.secrets$
+^.vscode

.gitignore

@@ -56,3 +56,5 @@ docs
 .Rdata
 .secrets
 .quarto
+
+.vscode/

DESCRIPTION

@@ -51,7 +51,7 @@ Remotes:
     hubverse-org/hubExamples,
     hubverse-org/hubUtils
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 URL: https://hubverse-org.github.io/hubEvals/
 Depends: 
     R (>= 2.10)

R/score_model_out.R

@@ -1,9 +1,13 @@
-#' Score model output predictions with a single `output_type` against observed data
+#' Score model output predictions
+#'
+#' Scores model outputs with a single `output_type` against observed data.
 #'
 #' @param model_out_tbl Model output tibble with predictions
 #' @param target_observations Observed 'ground truth' data to be compared to
 #' predictions
-#' @param metrics Optional character vector of scoring metrics to compute. See details for more.
+#' @param metrics Character vector of scoring metrics to compute. If `NULL`
+#' (the default), appropriate metrics are chosen automatically. See details
+#' for more.
 #' @param summarize Boolean indicator of whether summaries of forecast scores
 #' should be computed. Defaults to `TRUE`.
 #' @param by Character vector naming columns to summarize by. For example,
@@ -13,43 +17,40 @@
 #' vector of levels for pmf forecasts, in increasing order of the levels. For
 #' all other output types, this is ignored.
 #'
-#' @details If `metrics` is `NULL` (the default), this function chooses
-#' appropriate metrics based on the `output_type` contained in the `model_out_tbl`:
-#'
-#' \itemize{
-#' \item For `output_type == "quantile"`, we use the default metrics provided by
-#' `scoringutils`:
-#' `r names(scoringutils::get_metrics(scoringutils::example_quantile))`
-#' \item For `output_type == "pmf"` and `output_type_id_order` is `NULL` (indicating
-#' that the predicted variable is a nominal variable), we use the default metric
-#' provided by `scoringutils`:
-#' `r names(scoringutils::get_metrics(scoringutils::example_nominal))`
-#'   \item For `output_type == "median"`, we use "ae_point"
-#'   \item For `output_type == "mean"`, we use "se_point"
-#' }
-#'
-#' Alternatively, a character vector of scoring metrics can be provided. In this
-#' case, the following options are supported:
-#'   - `output_type == "median"` and `output_type == "mean"`:
-#'     - "ae_point": absolute error of a point prediction (generally recommended for the median)
-#'     - "se_point": squared error of a point prediction (generally recommended for the mean)
-#'   - `output_type == "quantile"`:
-#'     - "ae_median": absolute error of the predictive median (i.e., the quantile at probability level 0.5)
-#'     - "wis": weighted interval score (WIS) of a collection of quantile predictions
-#'     - "overprediction": The component of WIS measuring the extent to which
-#' predictions fell above the observation.
-#'     - "underprediction": The component of WIS measuring the extent to which
-#' predictions fell below the observation.
-#'     - "dispersion":  The component of WIS measuring the dispersion of forecast
-#' distributions.
-#'     - "interval_coverage_XX": interval coverage at the "XX" level. For example,
+#' @details
+#' Default metrics are provided by the `scoringutils` package. You can select
+#' metrics by passing in a character vector of metric names to the `metrics`
+#' argument.
+#'
+#' The following metrics can be selected (all are used by default) for the
+#' different `output_type`s:
+#'
+#' **Quantile forecasts:** (`output_type == "quantile"`)
+#' `r exclude <- c("interval_coverage_50", "interval_coverage_90")`
+#' `r metrics <- scoringutils::get_metrics(scoringutils::example_quantile, exclude = exclude)`
+#' `r paste("- ", names(metrics), collapse = "\n")`
+#' - "interval_coverage_XX": interval coverage at the "XX" level. For example,
 #' "interval_coverage_95" is the 95% interval coverage rate, which would be calculated
 #' based on quantiles at the probability levels 0.025 and 0.975.
-#'   - `output_type == "pmf"`:
-#'     - "log_score": log score
 #'
-#' See [scoringutils::get_metrics()] for more details on the default metrics
-#' used by `scoringutils`.
+#' See [scoringutils::get_metrics.forecast_quantile] for details.
+#'
+#' **Nominal forecasts:** (`output_type == "pmf"` and `output_type_id_order` is `NULL`)
+#'
+#' `r paste("- ", names(scoringutils::get_metrics(scoringutils::example_nominal)), collapse = "\n")`
+#'
+#' (scoring for ordinal forecasts will be added in the future).
+#'
+#' See [scoringutils::get_metrics.forecast_nominal] for details.
+#'
+#' **Median forecasts:** (`output_type == "median"`)
+#'
+#' - ae_point: absolute error of the point forecast (recommended for the median, see Gneiting (2011))
+#'
+#' See [scoringutils::get_metrics.forecast_point] for details.
+#'
+#' **Mean forecasts:** (`output_type == "mean"`)
+#' - `se_point`: squared error of the point forecast (recommended for the mean, see Gneiting (2011))
 #'
 #' @examplesIf requireNamespace("hubExamples", quietly = TRUE)
 #' # compute WIS and interval coverage rates at 80% and 90% levels based on
@@ -77,7 +78,11 @@
 #' )
 #' head(pmf_scores)
 #'
-#' @return forecast_quantile
+#' @return A data.table with scores
+#'
+#' @references
+#' #' Making and Evaluating Point Forecasts, Gneiting, Tilmann, 2011,
+#' Journal of the American Statistical Association.
 #'
 #' @export
 score_model_out <- function(model_out_tbl, target_observations, metrics = NULL,
@@ -87,9 +92,6 @@ score_model_out <- function(model_out_tbl, target_observations, metrics = NULL,
   # also, retrieve that output_type
   output_type <- validate_output_type(model_out_tbl)
 
-  # get/validate the scoring metrics
-  metrics <- get_metrics(metrics, output_type, output_type_id_order)
-
   # assemble data for scoringutils
   su_data <- switch(output_type,
     quantile = transform_quantile_model_out(model_out_tbl, target_observations),
@@ -99,16 +101,15 @@ score_model_out <- function(model_out_tbl, target_observations, metrics = NULL,
     NULL # default, should not happen because of the validation above
   )
 
+  # get/validate the scoring metrics
+  metrics <- get_metrics(forecast = su_data, output_type = output_type, select = metrics)
+
   # compute scores
   scores <- scoringutils::score(su_data, metrics)
 
   # switch back to hubverse naming conventions for model name
   scores <- dplyr::rename(scores, model_id = "model")
 
-  # if present, drop predicted and observed columns
-  drop_cols <- c("predicted", "observed")
-  scores <- scores[!colnames(scores) %in% drop_cols]
-
   # if requested, summarize scores
   if (summarize) {
     scores <- scoringutils::summarize_scores(scores = scores, by = by)
@@ -118,138 +119,56 @@ score_model_out <- function(model_out_tbl, target_observations, metrics = NULL,
 }
 
 
-#' Get metrics if user didn't specify anything; otherwise, process
-#' and validate user inputs
-#'
-#' @inheritParams score_model_out
-#'
-#' @return a list of metric functions as required by scoringutils::score()
-#'
-#' @noRd
-get_metrics <- function(metrics, output_type, output_type_id_order) {
-  if (is.null(metrics)) {
-    return(get_metrics_default(output_type, output_type_id_order))
-  } else if (is.character(metrics)) {
-    return(get_metrics_character(metrics, output_type))
-  } else {
-    cli::cli_abort(
-      "{.arg metrics} must be either `NULL` or a character vector of supported metrics."
-    )
-  }
-}
-
-
-#' Default metrics if user didn't specify anything
+#' Get scoring metrics
 #'
+#' @param forecast A scoringutils `forecast` object (see
+#' [scoringutils::as_forecast()] for details).
 #' @inheritParams score_model_out
 #'
 #' @return a list of metric functions as required by scoringutils::score()
 #'
 #' @noRd
-get_metrics_default <- function(output_type, output_type_id_order) {
-  metrics <- switch(output_type,
-    quantile = scoringutils::get_metrics(scoringutils::example_quantile),
-    pmf = scoringutils::get_metrics(scoringutils::example_nominal),
-    mean = scoringutils::get_metrics(scoringutils::example_point, select = "se_point"),
-    median = scoringutils::get_metrics(scoringutils::example_point, select = "ae_point"),
-    NULL # default
-  )
-  if (is.null(metrics)) {
-    # we have already validated `output_type`, so this case should not be
-    # triggered; this case is just double checking in case we add something new
-    # later, to ensure we update this function.
-    supported_types <- c("mean", "median", "pmf", "quantile") # nolint object_use_linter
-    cli::cli_abort(
-      "Provided `model_out_tbl` contains `output_type` {.val {output_type}};
-      hubEvals currently only supports the following types:
-      {.val {supported_types}}"
-    )
-  }
-
-  return(metrics)
-}
+get_metrics <- function(forecast, output_type, select = NULL) {
+  forecast_type <- class(forecast)[1]
 
-
-#' Convert character vector of metrics to list of functions
-#'
-#' @inheritParams score_model_out
-#'
-#' @return a list of metric functions as required by scoringutils::score()
-#'
-#' @noRd
-get_metrics_character <- function(metrics, output_type) {
-  if (output_type == "quantile") {
+  # process quantile metrics separately to allow better selection of interval
+  # coverage metrics
+  if (forecast_type == "forecast_quantile") {
     # split into metrics for interval coverage and others
-    interval_metric_inds <- grepl(pattern = "^interval_coverage_[[:digit:]][[:digit:]]$", metrics)
-    interval_metrics <- metrics[interval_metric_inds]
-    other_metrics <- metrics[!interval_metric_inds]
+    interval_metric_inds <- grepl(pattern = "interval_coverage_", select)
+    interval_metrics <- select[interval_metric_inds]
+    other_metrics <- select[!interval_metric_inds]
 
-    # validate metrics
-    valid_metrics <- c("ae_median", "wis", "overprediction", "underprediction", "dispersion")
-    invalid_metrics <- other_metrics[!other_metrics %in% valid_metrics]
-    error_if_invalid_metrics(
-      valid_metrics = c(valid_metrics, "interval_coverage_XY"),
-      invalid_metrics = invalid_metrics,
-      output_type = output_type,
-      comment = c("i" = "NOTE: `XY` denotes the coverage level, e.g. {.val interval_coverage_95}.")
-    )
+    other_metric_fns <- scoringutils::get_metrics(forecast, select = other_metrics)
 
-    # assemble metric functions
+    # assemble interval coverage functions
     interval_metric_fns <- lapply(
       interval_metrics,
       function(metric) {
-        level <- as.integer(substr(metric, 19, 20))
+        level_str <- substr(metric, 19, nchar(metric))
+        level <- suppressWarnings(as.numeric(level_str))
+        if (is.na(level) || level <= 0 || level >= 100) {
+          stop(paste(
+            "Invalid interval coverage level:", level_str,
+            "- must be a number between 0 and 100 (exclusive)"
+          ))
+        }
         return(purrr::partial(scoringutils::interval_coverage, interval_range = level))
       }
     )
     names(interval_metric_fns) <- interval_metrics
 
-    other_metric_fns <- scoringutils::get_metrics(
-      scoringutils::example_quantile,
-      select = other_metrics
-    )
-
-    metric_fns <- c(other_metric_fns, interval_metric_fns)[metrics]
-    metrics <- metric_fns
-  } else if (output_type == "pmf") {
-    valid_metrics <- c("log_score")
-    invalid_metrics <- metrics[!metrics %in% valid_metrics]
-    error_if_invalid_metrics(valid_metrics, invalid_metrics, output_type)
-
-    metrics <- scoringutils::get_metrics(
-      scoringutils::example_nominal,
-      select = metrics
-    )
-  } else if (output_type %in% c("median", "mean")) {
-    valid_metrics <- c("ae_point", "se_point")
-    invalid_metrics <- metrics[!metrics %in% valid_metrics]
-    error_if_invalid_metrics(valid_metrics, invalid_metrics, output_type)
-
-    metrics <- scoringutils::get_metrics(
-      scoringutils::example_point,
-      select = metrics
-    )
-  } else {
-    # we have already validated `output_type`, so this case should not be
-    # triggered; this case is just double checking in case we add something new
-    # later, to ensure we update this function.
-    error_if_invalid_output_type(output_type)
+    metric_fns <- c(other_metric_fns, interval_metric_fns)
+    return(metric_fns)
   }
 
-  return(metrics)
-}
-
-
-error_if_invalid_metrics <- function(valid_metrics, invalid_metrics, output_type, comment = NULL) {
-  n <- length(invalid_metrics)
-  if (n > 0) {
-    cli::cli_abort(
-      c(
-        "`metrics` had {n} unsupported metric{?s} for
-        {.arg output_type} {.val {output_type}}: {.strong {.val {invalid_metrics}}};
-        supported metrics include {.val {valid_metrics}}.",
-        comment
-      )
-    )
+  # leave validation of user selection to scoringutils
+  metric_fns <- scoringutils::get_metrics(forecast, select = select)
+  if (output_type == "mean") {
+    metric_fns <- scoringutils::select_metrics(metric_fns, "se_point")
+  } else if (output_type == "median") {
+    metric_fns <- scoringutils::select_metrics(metric_fns, "ae_point")
   }
+
+  return(metric_fns)
 }