tests: snapshot rest of the errors

NAMESPACE

@@ -273,24 +273,12 @@ importFrom(rlang,caller_arg)
 importFrom(rlang,caller_env)
 importFrom(rlang,enquo)
 importFrom(rlang,enquos)
-importFrom(rlang,env_get_list)
 importFrom(rlang,expr)
 importFrom(rlang,global_env)
 importFrom(rlang,inject)
-importFrom(rlang,is_call)
-importFrom(rlang,is_character)
-importFrom(rlang,is_closure)
-importFrom(rlang,is_environment)
-importFrom(rlang,is_formula)
-importFrom(rlang,is_function)
-importFrom(rlang,is_list)
 importFrom(rlang,is_logical)
-importFrom(rlang,is_missing)
 importFrom(rlang,is_null)
-importFrom(rlang,is_string)
-importFrom(rlang,is_symbol)
 importFrom(rlang,is_true)
-importFrom(rlang,is_vector)
 importFrom(rlang,set_names)
 importFrom(rlang,sym)
 importFrom(stats,as.formula)

R/epipredict-package.R

@@ -1,9 +1,9 @@
 ## usethis namespace: start
 #' @import epiprocess parsnip
-#' @importFrom checkmate assert assert_character assert_int assert_scalar
-#' @importFrom checkmate assert_logical assert_numeric assert_number
-#' @importFrom checkmate assert_integer assert_integerish
-#' @importFrom checkmate assert_date assert_function assert_class
+#' @importFrom checkmate assert_class assert_numeric
+#' @importFrom checkmate test_character test_date test_function
+#' @importFrom checkmate test_integerish test_logical
+#' @importFrom checkmate test_numeric test_scalar
 #' @importFrom cli cli_abort cli_warn
 #' @importFrom dplyr arrange across all_of any_of bind_cols bind_rows group_by
 #' @importFrom dplyr summarize filter mutate select left_join rename ungroup

tests/testthat/_snaps/arx_args_list.md

@@ -0,0 +1,152 @@
+# arx_args checks inputs
+
+    Code
+      arx_args_list(ahead = c(0, 4))
+    Condition
+      Error in `arx_args_list()`:
+      ! `ahead` must be a scalar.
+
+---
+
+    Code
+      arx_args_list(n_training = c(28, 65))
+    Condition
+      Error in `arx_args_list()`:
+      ! `n_training` must be a scalar.
+
+---
+
+    Code
+      arx_args_list(ahead = -1)
+    Condition
+      Error in `arx_args_list()`:
+      ! `ahead` must be a non-negative integer.
+
+---
+
+    Code
+      arx_args_list(ahead = 1.5)
+    Condition
+      Error in `arx_args_list()`:
+      ! `ahead` must be a non-negative integer.
+
+---
+
+    Code
+      arx_args_list(n_training = -1)
+    Condition
+      Error in `arx_args_list()`:
+      ! `n_training` must be a strictly positive number.
+
+---
+
+    Code
+      arx_args_list(n_training = 1.5)
+    Condition
+      Error in `arx_args_list()`:
+      ! `n_training` must be a positive integer.
+
+---
+
+    Code
+      arx_args_list(lags = c(-1, 0))
+    Condition
+      Error in `arx_args_list()`:
+      ! `lags` must be non-negative integers.
+
+---
+
+    Code
+      arx_args_list(lags = list(c(1:5, 6.5), 2:8))
+    Condition
+      Error in `arx_args_list()`:
+      ! `lags` must be non-negative integers.
+
+---
+
+    Code
+      arx_args_list(symmetrize = 4)
+    Condition
+      Error in `arx_args_list()`:
+      ! `symmetrize` must be of type <logical>.
+
+---
+
+    Code
+      arx_args_list(nonneg = 4)
+    Condition
+      Error in `arx_args_list()`:
+      ! `nonneg` must be of type <logical>.
+
+---
+
+    Code
+      arx_args_list(quantile_levels = -0.1)
+    Condition
+      Error in `arx_args_list()`:
+      ! `quantile_levels` must lie in [0, 1].
+
+---
+
+    Code
+      arx_args_list(quantile_levels = 1.1)
+    Condition
+      Error in `arx_args_list()`:
+      ! `quantile_levels` must lie in [0, 1].
+
+---
+
+    Code
+      arx_args_list(target_date = "2022-01-01")
+    Condition
+      Error in `arx_args_list()`:
+      ! `target_date` must be a date.
+
+---
+
+    Code
+      arx_args_list(n_training_min = "de")
+    Condition
+      Error in `arx_args_list()`:
+      ! `...` must be empty.
+      x Problematic argument:
+      * n_training_min = "de"
+
+---
+
+    Code
+      arx_args_list(epi_keys = 1)
+    Condition
+      Error in `arx_args_list()`:
+      ! `...` must be empty.
+      x Problematic argument:
+      * epi_keys = 1
+
+# arx forecaster disambiguates quantiles
+
+    Code
+      compare_quantile_args(alist, tlist)
+    Condition
+      Error in `compare_quantile_args()`:
+      ! You have specified different, non-default, quantiles in the trainier and `arx_args` options.
+      i Please only specify quantiles in one location.
+
+# arx_lags_validator handles named & unnamed lists as expected
+
+    Code
+      arx_lags_validator(pred_vec, lags_finit_fn_switch2)
+    Condition
+      Error in `arx_lags_validator()`:
+      ! You have requested 2 predictor(s) but 3 different lags.
+      i Lags must be a vector or a list with length == number of predictors.
+
+---
+
+    Code
+      arx_lags_validator(pred_vec, lags_init_other_name)
+    Condition
+      Error in `arx_lags_validator()`:
+      ! If lags is a named list, then all predictors must be present.
+      i The predictors are `death_rate` and `case_rate`.
+      i So lags is missing `case_rate`'.
+

tests/testthat/_snaps/arx_cargs_list.md

@@ -0,0 +1,92 @@
+# arx_class_args checks inputs
+
+    Code
+      arx_class_args_list(ahead = c(0, 4))
+    Condition
+      Error in `arx_class_args_list()`:
+      ! `ahead` must be a scalar.
+
+---
+
+    Code
+      arx_class_args_list(n_training = c(28, 65))
+    Condition
+      Error in `arx_class_args_list()`:
+      ! `n_training` must be a scalar.
+
+---
+
+    Code
+      arx_class_args_list(ahead = -1)
+    Condition
+      Error in `arx_class_args_list()`:
+      ! `ahead` must be a non-negative integer.
+
+---
+
+    Code
+      arx_class_args_list(ahead = 1.5)
+    Condition
+      Error in `arx_class_args_list()`:
+      ! `ahead` must be a non-negative integer.
+
+---
+
+    Code
+      arx_class_args_list(n_training = -1)
+    Condition
+      Error in `arx_class_args_list()`:
+      ! `n_training` must be a strictly positive number.
+
+---
+
+    Code
+      arx_class_args_list(n_training = 1.5)
+    Condition
+      Error in `arx_class_args_list()`:
+      ! `n_training` must be a positive integer.
+
+---
+
+    Code
+      arx_class_args_list(lags = c(-1, 0))
+    Condition
+      Error in `arx_class_args_list()`:
+      ! `lags` must be non-negative integers.
+
+---
+
+    Code
+      arx_class_args_list(lags = list(c(1:5, 6.5), 2:8))
+    Condition
+      Error in `arx_class_args_list()`:
+      ! `lags` must be non-negative integers.
+
+---
+
+    Code
+      arx_class_args_list(target_date = "2022-01-01")
+    Condition
+      Error in `arx_class_args_list()`:
+      ! `target_date` must be a date.
+
+---
+
+    Code
+      arx_class_args_list(n_training_min = "de")
+    Condition
+      Error in `arx_class_args_list()`:
+      ! `...` must be empty.
+      x Problematic argument:
+      * n_training_min = "de"
+
+---
+
+    Code
+      arx_class_args_list(epi_keys = 1)
+    Condition
+      Error in `arx_class_args_list()`:
+      ! `...` must be empty.
+      x Problematic argument:
+      * epi_keys = 1
+

tests/testthat/_snaps/bake-method.md

@@ -0,0 +1,9 @@
+# bake method works in all cases
+
+    Code
+      bake(prep(r, edf), NULL, composition = "matrix")
+    Condition
+      Error in `hardhat::recompose()`:
+      ! `data` must only contain numeric columns.
+      i These columns aren't numeric: "geo_value" and "time_value".
+

tests/testthat/_snaps/check-training-set.md

@@ -0,0 +1,20 @@
+# training set validation works
+
+    Code
+      validate_meta_match(t1, template, "geo_type", "abort")
+    Condition
+      Error in `validate_meta_match()`:
+      ! The `geo_type` of the training data appears to be different from that
+      used to construct the recipe. This may result in unexpected consequences.
+      i Training `geo_type` is 'county'.
+      i Originally, it was 'state'.
+
+---
+
+    Code
+      epi_check_training_set(t4, rec)
+    Condition
+      Error in `epi_check_training_set()`:
+      ! The recipe specifies keys which are not in the training data.
+      i The training set is missing columns for missing_col.
+

tests/testthat/_snaps/check_enough_train_data.md

@@ -0,0 +1,46 @@
+# check_enough_train_data works on pooled data
+
+    Code
+      epi_recipe(toy_epi_df) %>% check_enough_train_data(x, y, n = 2 * n + 1,
+      drop_na = FALSE) %>% prep(toy_epi_df) %>% bake(new_data = NULL)
+    Condition
+      Error in `prep()`:
+      ! The following columns don't have enough data to predict: x and y.
+
+---
+
+    Code
+      epi_recipe(toy_epi_df) %>% check_enough_train_data(x, y, n = 2 * n - 1,
+      drop_na = TRUE) %>% prep(toy_epi_df) %>% bake(new_data = NULL)
+    Condition
+      Error in `prep()`:
+      ! The following columns don't have enough data to predict: x and y.
+
+# check_enough_train_data works on unpooled data
+
+    Code
+      epi_recipe(toy_epi_df) %>% check_enough_train_data(x, y, n = n + 1, epi_keys = "geo_value",
+      drop_na = FALSE) %>% prep(toy_epi_df) %>% bake(new_data = NULL)
+    Condition
+      Error in `prep()`:
+      ! The following columns don't have enough data to predict: x and y.
+
+---
+
+    Code
+      epi_recipe(toy_epi_df) %>% check_enough_train_data(x, y, n = 2 * n - 3,
+      epi_keys = "geo_value", drop_na = TRUE) %>% prep(toy_epi_df) %>% bake(new_data = NULL)
+    Condition
+      Error in `prep()`:
+      ! The following columns don't have enough data to predict: x and y.
+
+# check_enough_train_data works with all_predictors() downstream of constructed terms
+
+    Code
+      epi_recipe(toy_epi_df) %>% step_epi_lag(x, lag = c(1, 2)) %>%
+        check_enough_train_data(all_predictors(), y, n = 2 * n - 5) %>% prep(
+        toy_epi_df) %>% bake(new_data = NULL)
+    Condition
+      Error in `prep()`:
+      ! The following columns don't have enough data to predict: lag_1_x, lag_2_x, and y.
+