rebase fixes, error classes, unskip latency tests

Author: dsweber2

NAMESPACE

@@ -251,8 +251,8 @@ importFrom(quantreg,rq)
 importFrom(recipes,bake)
 importFrom(recipes,detect_step)
 importFrom(recipes,prep)
-importFrom(rlang,"!!!")
 importFrom(recipes,recipes_eval_select)
+importFrom(rlang,"!!!")
 importFrom(rlang,"!!")
 importFrom(rlang,"%@%")
 importFrom(rlang,"%||%")
@@ -262,6 +262,7 @@ importFrom(rlang,caller_env)
 importFrom(rlang,enquos)
 importFrom(rlang,global_env)
 importFrom(rlang,inject)
+importFrom(rlang,is_empty)
 importFrom(rlang,is_logical)
 importFrom(rlang,is_null)
 importFrom(rlang,is_true)

R/canned-epipred.R

@@ -133,7 +133,8 @@ print.canned_epipred <- function(x, name, ...) {
       purrr::map("columns") %>%
       reduce(c)
     latency_per_base_col <- latency_step$latency_table %>%
-      filter(col_name %in% valid_columns) %>% mutate(latency = abs(latency))
+      filter(col_name %in% valid_columns) %>%
+      mutate(latency = abs(latency))
     if (latency_step$method != "locf" && nrow(latency_per_base_col) > 1) {
       intro_text <- glue::glue("{type_str} adjusted per column: ")
     } else if (latency_step$method != "locf") {

R/cdc_baseline_forecaster.R

@@ -79,7 +79,8 @@ cdc_baseline_forecaster <- function(
 
 
   latest <- get_test_data(
-    epi_recipe(epi_data), epi_data)
+    epi_recipe(epi_data), epi_data
+  )
 
   f <- frosting() %>%
     layer_predict() %>%

R/epi_shift.R

@@ -45,16 +45,17 @@ add_shifted_columns <- function(new_data, object, amount) {
   shift_sign_lat <- attributes(new_data)$metadata$shift_sign
   if (!is.null(latency_table) &&
     shift_sign_lat == sign_shift) {
-    #TODO this doesn't work on lags of transforms
+    # TODO this doesn't work on lags of transforms
     rel_latency <- latency_table %>% filter(col_name %in% object$columns)
   } else {
     rel_latency <- tibble(col_name = object$columns, latency = 0L)
   }
-  grid <- expand_grid(col = object$columns, amount = sign_shift *amount) %>%
+  grid <- expand_grid(col = object$columns, amount = sign_shift * amount) %>%
     left_join(rel_latency, by = join_by(col == col_name), ) %>%
     tidyr::replace_na(list(latency = 0)) %>%
     dplyr::mutate(
-      shift_val = amount + latency) %>%
+      shift_val = amount + latency
+    ) %>%
     mutate(
       newname = glue::glue("{object$prefix}{abs(shift_val)}_{col}"), # name is always positive
       amount = NULL,

R/get_test_data.R

@@ -11,18 +11,9 @@
 #' used if growth rate calculations are requested by the recipe. This is
 #' calculated internally.
 #'
-#' It also optionally fills missing values
-#' using the last-observation-carried-forward (LOCF) method. If this
-#' is not possible (say because there would be only `NA`'s in some location),
-#' it will produce an error suggesting alternative options to handle missing
-#' values with more advanced techniques.
-#'
 #' @param recipe A recipe object.
 #' @param x An epi_df. The typical usage is to
 #'   pass the same data as that used for fitting the recipe.
-#' @param forecast_date By default, this is set to the maximum
-#'   `time_value` in `x`. But if there is data latency such that recent `NA`'s
-#'   should be filled, this may be _after_ the last available `time_value`.
 #'
 #' @return An object of the same type as `x` with columns `geo_value`, `time_value`, any additional
 #'   keys, as well other variables in the original dataset.
@@ -36,9 +27,7 @@
 #' @importFrom rlang %@%
 #' @export
 
-get_test_data <- function(
-    recipe,
-    x) {
+get_test_data <- function(recipe, x) {
   if (!is_epi_df(x)) cli::cli_abort("`x` must be an `epi_df`.")
 
   check <- hardhat::check_column_names(x, colnames(recipe$template))
@@ -64,7 +53,10 @@ get_test_data <- function(
       "!" = "but `x` contains only {avail_recent}."
     ))
   }
-  max_time_value <- x %>% na.omit %>% pull(time_value) %>% max
+  max_time_value <- x %>%
+    na.omit() %>%
+    pull(time_value) %>%
+    max()
   x <- arrange(x, time_value)
   groups <- kill_time_value(epi_keys(recipe))
 

R/step_adjust_latency.R

@@ -86,7 +86,7 @@
 #' jhu_fit
 #'
 #' @importFrom recipes detect_step
-#' @importFrom rlang enquos
+#' @importFrom rlang enquos is_empty
 step_adjust_latency <-
   function(recipe,
            ...,
@@ -106,39 +106,43 @@ step_adjust_latency <-
            id = recipes::rand_id("adjust_latency")) {
     arg_is_chr_scalar(id, method)
     if (!is_epi_recipe(recipe)) {
-      cli::cli_abort("This recipe step can only operate on an {.cls epi_recipe}.")
+      cli::cli_abort("This recipe step can only operate on an {.cls epi_recipe}.", class = "epipredict__step_adjust_latency__epi_recipe_only")
     }
     if (!is.null(columns)) {
       cli::cli_abort(c("The `columns` argument must be `NULL`.",
         i = "Use `tidyselect` methods to choose columns to lag."
-      ))
+      ), class = "epipredict__step_adjust_latency__cols_not_null")
     }
     if ((method == "extend_ahead") && (detect_step(recipe, "epi_ahead"))) {
       cli::cli_warn(
-        "If `method` is {.val extend_ahead}, then the previous `step_epi_ahead` won't be modified."
+        "If `method` is {.val extend_ahead}, then the previous `step_epi_ahead` won't be modified.",
+        class = "epipredict__step_adjust_latency__misordered_step_warning"
       )
     } else if ((method == "extend_lags") && detect_step(recipe, "epi_lag")) {
       cli::cli_warn(
         "If `method` is {.val extend_lags} or {.val locf},
-then the previous `step_epi_lag`s won't work with modified data."
+then the previous `step_epi_lag`s won't work with modified data.",
+        class = "epipredict__step_adjust_latency__misordered_step_warning"
       )
     } else if ((method == "locf") && (length(recipe$steps) > 0)) {
-      cli::cli_warn("There are steps before `step_adjust_latency`. With the method {.val locf}, it is recommended to include this step before any others")
+      cli::cli_warn("There are steps before `step_adjust_latency`. With the method {.val locf}, it is recommended to include this step before any others",
+        class = "epipredict__step_adjust_latency__misordered_step_warning"
+      )
     }
     if (detect_step(recipe, "naomit")) {
       cli::cli_abort("adjust_latency needs to occur before any `NA` removal,
-                      as columns may be moved around")
+                      as columns may be moved around", class = "epipredict__step_adjust_latency__post_NA_error")
     }
     if (!is.null(fixed_latency) && !is.null(fixed_forecast_date)) {
       cli::cli_abort("Only one of `fixed_latency` and `fixed_forecast_date`
- can be non-`NULL` at a time!")
+ can be non-`NULL` at a time!", class = "epipredict__step_adjust_latency__too_many_args_error")
     }
     if (length(fixed_latency > 1)) {
       template <- recipe$template
       data_names <- names(template)[!names(template) %in% epi_keys(template)]
       wrong_names <- names(fixed_latency)[!names(fixed_latency) %in% data_names]
       if (length(wrong_names) > 0) {
-        cli::cli_abort("{.val fixed_latency} contains names not in the template dataset: {wrong_names}")
+        cli::cli_abort("{.val fixed_latency} contains names not in the template dataset: {wrong_names}", class = "epipredict__step_adjust_latency__undefined_names_error")
       }
     }
 
@@ -258,8 +262,8 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
         "{time_type}."
       ),
       "i" = "latency: {latency_table$latency[[i_latency]]}",
-      "i" = "`max_time` = {max_time} -> `forecast_date` = {forecast_date}"
-    ))
+      "i" = "`max_time` = {max(training$time_value)} -> `forecast_date` = {forecast_date}"
+    ), class = "epipredict__prep.step_latency__very_large_latency")
   }
 
   step_adjust_latency_new(

R/utils-latency.R

@@ -38,11 +38,14 @@ set_forecast_date <- function(new_data, info, epi_keys_checked, latency) {
     pull(variable)
   # make sure that there's enough column names
   if (length(original_columns) < 3) {
-    cli::cli_abort(glue::glue(
-      "The original columns of `time_value`, ",
-      "`geo_value` and at least one signal. The current colums are \n",
-      paste(capture.output(object$info), collapse = "\n\n")
-    ))
+    cli::cli_abort(
+      glue::glue(
+        "The original columns of `time_value`, ",
+        "`geo_value` and at least one signal. The current colums are \n",
+        paste(capture.output(object$info), collapse = "\n\n")
+      ),
+      class = "epipredict__set_forecast_date__too_few_data_columns"
+    )
   }
   # the source data determines the actual time_values
   # these are the non-na time_values;
@@ -65,25 +68,34 @@ set_forecast_date <- function(new_data, info, epi_keys_checked, latency) {
   }
   # make sure the as_of is sane
   if (!inherits(forecast_date, class(max_time)) & !inherits(forecast_date, "POSIXt")) {
-    cli::cli_abort(paste(
-      "the data matrix `forecast_date` value is {forecast_date}, ",
-      "and not a valid `time_type` with type ",
-      "matching `time_value`'s type of ",
-      "{class(max_time)}."
-    ))
+    cli::cli_abort(
+      paste(
+        "the data matrix `forecast_date` value is {forecast_date}, ",
+        "and not a valid `time_type` with type ",
+        "matching `time_value`'s type of ",
+        "{class(max_time)}."
+      ),
+      class = "epipredict__set_forecast_date__wrong_time_value_type_error"
+    )
   }
   if (is.null(forecast_date) || is.na(forecast_date)) {
-    cli::cli_warn(paste(
-      "epi_data's `forecast_date` was {forecast_date}, setting to ",
-      "the latest time value, {max_time}."
-    ))
+    cli::cli_warn(
+      paste(
+        "epi_data's `forecast_date` was {forecast_date}, setting to ",
+        "the latest time value, {max_time}."
+      ),
+      class = "epipredict__set_forecast_date__max_time_warning"
+    )
     forecast_date <- max_time
   } else if (forecast_date < max_time) {
-    cli::cli_abort(paste(
-      "`forecast_date` ({(forecast_date)}) is before the most ",
-      "recent data ({max_time}). Remove before ",
-      "predicting."
-    ))
+    cli::cli_abort(
+      paste(
+        "`forecast_date` ({(forecast_date)}) is before the most ",
+        "recent data ({max_time}). Remove before ",
+        "predicting."
+      ),
+      class = "epipredict__set_forecast_date__misordered_forecast_date_error"
+    )
   }
   # TODO cover the rest of the possible types for as_of and max_time...
   if (inherits(max_time, "Date")) {

man/get_test_data.Rd

@@ -98,7 +98,7 @@ test_that("arx_forecaster snapshots", {
 
 test_that("arx_forecaster output format snapshots", {
   jhu <- case_death_rate_subset %>%
-    dplyr::filter(time_value >= as.Date("2021-12-01")) 
+    dplyr::filter(time_value >= as.Date("2021-12-01"))
   out1 <- arx_forecaster(
     jhu, "death_rate",
     c("case_rate", "death_rate")