fix transform_pmf_model_out (#57)

* remove `model` parameter from scoringutils call

This change was introduced in
epiforecasts/scoringutils#915 and subsequently
broke our tests.

* hide warnings from tests

A whole bunch of new warnings started popping up because scoringutils
was trying to take the mean of character columns.

Wrapping everything in `suppressWarnings()` helps.

* okay linter

DESCRIPTION

@@ -22,7 +22,8 @@ Authors@R: c(
            role = c("aut")),
     person(given = "Zhian",
            family = "Kamvar",
-           email = "[email protected] ",
+           email = "[email protected]",
+           comment = c(ORCID = "0000-0003-1458-7108"),
            role = c("ctb")),
     person(given = "Becky", 
            family = "Sweger",

R/transform_pmf_model_out.R

@@ -56,7 +56,6 @@ transform_pmf_model_out <- function(model_out_tbl, target_observations, output_t
     forecast_unit = c("model", task_id_cols),
     observed = "observation",
     predicted = "value",
-    model = "model",
     predicted_label = "output_type_id"
   )
 

tests/testthat/test-score_model_out.R

@@ -393,32 +393,34 @@ test_that("score_model_out works with all kinds of interval levels are requested
     regexp = "must be a number between 0 and 100"
   )
 
-  expect_warning(
-    score_model_out(
-      model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "quantile"),
-      target_observations = forecast_target_observations,
-      metrics = "interval_coverage_55"
-    ),
-    "To compute the interval coverage for an interval range of" #scoringutils warning
-  )
+  suppressWarnings({
+    expect_warning(
+      score_model_out(
+        model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "quantile"),
+        target_observations = forecast_target_observations,
+        metrics = "interval_coverage_55"
+      ),
+      "To compute the interval coverage for an interval range of" #scoringutils warning
+    )
 
-  expect_error(
-    score_model_out(
-      model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "quantile"),
-      target_observations = forecast_target_observations,
-      metrics = "interval_coverage_100"
-    ),
-    regexp = "must be a number between 0 and 100"
-  )
+    expect_error(
+      score_model_out(
+        model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "quantile"),
+        target_observations = forecast_target_observations,
+        metrics = "interval_coverage_100"
+      ),
+      regexp = "must be a number between 0 and 100"
+    )
 
-  expect_warning(
-    score_model_out(
-      model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "quantile"),
-      target_observations = forecast_target_observations,
-      metrics = "interval_coverage_5.3"
-    ),
-    "To compute the interval coverage for an interval range of" #scoringutils warning
-  )
+    expect_warning(
+      score_model_out(
+        model_out_tbl = forecast_outputs |> dplyr::filter(.data[["output_type"]] == "quantile"),
+        target_observations = forecast_target_observations,
+        metrics = "interval_coverage_5.3"
+      ),
+      "To compute the interval coverage for an interval range of" #scoringutils warning
+    )
+  })
 })