update tests to avoid random messages

tests/testthat/test-add_coverage.R

@@ -3,7 +3,7 @@ ex_coverage <- example_quantile[model == "EuroCOVIDhub-ensemble"]
 test_that("add_coverage() works as expected", {
   expect_message(
     cov <- add_coverage(example_quantile),
-    "Some rows contain NA values and will be removed in subsequent operations"
+    "Some rows containing NA values may be removed."
   )
 
   required_names <- c(

tests/testthat/test-available_forecasts.R

@@ -8,26 +8,26 @@ test_that("get_forecast_counts() works as expected", {
   expect_type(af$target_type, "character")
   expect_type(af$`count`, "integer")
   expect_equal(nrow(af[is.na(`count`)]), 0)
-  af <- get_forecast_counts(example_quantile, by = "model")
+  af <- get_forecast_counts(na.omit(example_quantile), by = "model")
   expect_equal(nrow(af), 4)
   expect_equal(af$`count`, c(256, 256, 128, 247))
 
   # Setting `collapse = c()` means that all quantiles and samples are counted
   af <- get_forecast_counts(
-    example_quantile,
+    na.omit(example_quantile),
      by = "model", collapse = c()
   )
   expect_equal(nrow(af), 4)
   expect_equal(af$`count`, c(5888, 5888, 2944, 5681))
 
   # setting by = NULL, the default, results in by equal to forecast unit
-  af <- get_forecast_counts(example_quantile)
+  af <- get_forecast_counts(na.omit(example_quantile))
   expect_equal(nrow(af), 50688)
 
   # check whether collapsing also works for model-based forecasts
-  af <- get_forecast_counts(example_integer, by = "model")
+  af <- get_forecast_counts(na.omit(example_integer), by = "model")
   expect_equal(nrow(af), 4)
 
-  af <- get_forecast_counts(example_integer, by = "model", collapse = c())
+  af <- get_forecast_counts(na.omit(example_integer), by = "model", collapse = c())
   expect_equal(af$count, c(10240, 10240, 5120, 9880))
 })

tests/testthat/test-check_forecasts.R

@@ -96,7 +96,7 @@ test_that("as_forecast() function throws an error when no predictions or observe
 test_that("output of check_forecasts() is accepted as input to score()", {
   check <- suppressMessages(as_forecast(example_binary))
   expect_no_error(
-    score_check <- score(check)
+    score_check <- score(na.omit(check))
   )
   expect_equal(score_check, suppressMessages(score(example_binary)))
 })

tests/testthat/test-convenience-functions.R

@@ -51,7 +51,7 @@ test_that("function set_forecast_unit() works", {
     example_quantile,
     c("location", "target_end_date", "target_type", "horizon", "model")
   )
-  scores2 <- score(ex2)
+  scores2 <- score(na.omit(ex2))
   scores2 <- scores2[order(location, target_end_date, target_type, horizon, model), ]
 
   expect_equal(scores1$interval_score, scores2$interval_score)

tests/testthat/test-get_-functions.R

@@ -14,7 +14,7 @@ test_that("get_forecast_unit() works as expected", {
       "forecast_date", "model", "horizon")
   )
 
-  data <- as_forecast(example_quantile)
+  data <- as_forecast(na.omit(example_quantile))
   ex <- data[, location := NULL]
   expect_warning(
     get_forecast_unit(ex, check_conflict = TRUE),
@@ -40,7 +40,7 @@ test_that("removing NA rows from data works as expected", {
   expect_equal(nrow(na.omit(ex)), 2)
 
   # test that attributes and classes are retained
-  ex <- as_forecast(example_integer)
+  ex <- as_forecast(na.omit(example_integer))
   expect_equal(
     class(na.omit(ex)),
     c("forecast_sample", "data.table", "data.frame")
@@ -193,7 +193,7 @@ test_that("get_forecast_type() works as expected", {
     fixed = TRUE
   )
 
-  data <- as_forecast(example_integer)
+  data <- as_forecast(na.omit(example_integer))
   attr(data, "forecast_type") <- "binary"
   expect_warning(
     get_forecast_type(data),

tests/testthat/test-plot_ranges.R

@@ -1,6 +1,7 @@
 m <- modifyList(metrics_no_cov_no_ae, list("bias" = NULL))
 
 sum_scores <- copy(example_quantile) %>%
+  na.omit() %>%
   .[, interval_range := scoringutils:::get_range_from_quantile(quantile)] %>%
   score(metrics = m) %>%
   summarise_scores(by = c("model", "target_type", "interval_range"))