Merge pull request #408 from cmu-delphi/prodFixes

Prod fixes

Author: dsweber2

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.1
+Version: 0.1.2
 Authors@R: c(
     person("Daniel J.", "McDonald", , "[email protected]", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "[email protected]", role = "aut"),

NEWS.md

@@ -6,8 +6,10 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 
 ## features
 - Add `step_adjust_latency`, which give several methods to adjust the forecast if the `forecast_date` is after the last day of data.
+- (temporary) ahead negative is allowed for `step_epi_ahead` until we have `step_epi_shift`
 
 ## bugfixes
+- shifting no columns results in no error for either `step_epi_ahead` and `step_epi_lag`
 
 # epipredict 0.1
 

R/epi_shift.R

@@ -42,6 +42,10 @@ get_sign <- function(object) {
 add_shifted_columns <- function(new_data, object) {
   grid <- object$shift_grid
 
+  if (nrow(object$shift_grid) == 0) {
+    # we're not shifting any rows, so this is a no-op
+    return(new_data)
+  }
   ## ensure no name clashes
   new_data_names <- colnames(new_data)
   intersection <- new_data_names %in% grid$newname

R/epi_workflow.R

@@ -167,6 +167,7 @@ predict.epi_workflow <- function(object, new_data, type = NULL, opts = list(), .
   components$forged <- hardhat::forge(new_data,
     blueprint = components$mold$blueprint
   )
+
   components$keys <- grab_forged_keys(components$forged, object, new_data)
   components <- apply_frosting(object, components, new_data, type = type, opts = opts, ...)
   components$predictions

R/step_adjust_latency.R

@@ -152,6 +152,12 @@
 #'
 #'   Note that this is a separate concern from different latencies across
 #'   different *data columns*, which is only handled by the choice of `method`.
+#' @param keys_to_ignore a list of character vectors. Set this to avoid using
+#'   specific key values in the `epi_keys_checked` to set latency. For example,
+#'   say you have two locations `pr` and `gu` which have useful training data,
+#'   but have stopped providing up-to-date information, and so are no longer
+#'   part of the test set. Setting `keys_to_ignore = list(geo_value = c("pr",
+#'   "gu"))` will exclude them from the latency calculation.
 #' @param fixed_latency either a positive integer, or a labeled positive integer
 #'   vector. Cannot be set at the same time as `fixed_forecast_date`. If
 #'   non-`NULL`, the amount to offset the ahead or lag by. If a single integer,
@@ -203,6 +209,7 @@ step_adjust_latency <-
              "extend_lags"
            ),
            epi_keys_checked = NULL,
+           keys_to_ignore = c(),
            fixed_latency = NULL,
            fixed_forecast_date = NULL,
            check_latency_length = TRUE,
@@ -228,6 +235,7 @@ step_adjust_latency <-
         metadata = NULL,
         method = method,
         epi_keys_checked = epi_keys_checked,
+        keys_to_ignore = keys_to_ignore,
         check_latency_length = check_latency_length,
         columns = NULL,
         skip = FALSE,
@@ -239,7 +247,7 @@ step_adjust_latency <-
 step_adjust_latency_new <-
   function(terms, role, trained, fixed_forecast_date, forecast_date, latency,
            latency_table, latency_sign, metadata, method, epi_keys_checked,
-           check_latency_length, columns, skip, id) {
+           keys_to_ignore, check_latency_length, columns, skip, id) {
     step(
       subclass = "adjust_latency",
       terms = terms,
@@ -253,6 +261,7 @@ step_adjust_latency_new <-
       metadata = metadata,
       method = method,
       epi_keys_checked = epi_keys_checked,
+      keys_to_ignore = keys_to_ignore,
       check_latency_length = check_latency_length,
       columns = columns,
       skip = skip,
@@ -271,7 +280,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
 
   latency_table <- get_latency_table(
     training, NULL, forecast_date, latency,
-    get_sign(x), x$epi_keys_checked, info, x$terms
+    get_sign(x), x$epi_keys_checked, x$keys_to_ignore, info, x$terms
   )
   # get the columns used, even if it's all of them
   terms_used <- x$terms
@@ -293,6 +302,7 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
     metadata = attributes(training)$metadata,
     method = x$method,
     epi_keys_checked = x$epi_keys_checked,
+    keys_to_ignore = x$keys_to_ignore,
     check_latency_length = x$check_latency_length,
     columns = recipes_eval_select(latency_table$col_name, training, info),
     skip = x$skip,
@@ -305,10 +315,8 @@ prep.step_adjust_latency <- function(x, training, info = NULL, ...) {
 #' @export
 bake.step_adjust_latency <- function(object, new_data, ...) {
   if (!inherits(new_data, "epi_df") || is.null(attributes(new_data)$metadata$as_of)) {
-    new_data <- as_epi_df(new_data)
+    new_data <- as_epi_df(new_data, as_of = object$forecast_date, other_keys = object$metadata$other_keys %||% character())
     attributes(new_data)$metadata <- object$metadata
-    attributes(new_data)$metadata$as_of <- object$forecast_date
-  } else {
     compare_bake_prep_latencies(object, new_data)
   }
   if (object$method == "locf") {

R/step_epi_shift.R

@@ -111,7 +111,6 @@ step_epi_ahead <-
         i = "Did you perhaps pass an integer in `...` accidentally?"
       ))
     }
-    arg_is_nonneg_int(ahead)
     arg_is_chr_scalar(prefix, id)
 
     recipes::add_step(

R/utils-latency.R

@@ -50,27 +50,18 @@ get_forecast_date <- function(new_data, info, epi_keys_checked, latency, columns
       )
     }
   }
+  max_time <- get_max_time(new_data, epi_keys_checked, columns)
   # the source data determines the actual time_values
-  # these are the non-na time_values;
-  # get the minimum value across the checked epi_keys' maximum time values
-  max_time <- new_data %>%
-    select(all_of(columns)) %>%
-    drop_na()
-  # null and "" don't work in `group_by`
-  if (!is.null(epi_keys_checked) && (epi_keys_checked != "")) {
-    max_time <- max_time %>% group_by(get(epi_keys_checked))
-  }
-  max_time <- max_time %>%
-    summarise(time_value = max(time_value)) %>%
-    pull(time_value) %>%
-    min()
   if (is.null(latency)) {
     forecast_date <- attributes(new_data)$metadata$as_of
   } else {
+    if (is.null(max_time)) {
+      cli_abort("max_time is null. This likely means there is one of {columns} that is all `NA`")
+    }
     forecast_date <- max_time + latency
   }
   # make sure the as_of is sane
-  if (!inherits(forecast_date, class(max_time)) & !inherits(forecast_date, "POSIXt")) {
+  if (!inherits(forecast_date, class(new_data$time_value)) & !inherits(forecast_date, "POSIXt")) {
     cli_abort(
       paste(
         "the data matrix `forecast_date` value is {forecast_date}, ",
@@ -84,13 +75,13 @@ get_forecast_date <- function(new_data, info, epi_keys_checked, latency, columns
   if (is.null(forecast_date) || is.na(forecast_date)) {
     cli_warn(
       paste(
-        "epi_data's `forecast_date` was {forecast_date}, setting to ",
-        "the latest time value, {max_time}."
+        "epi_data's `forecast_date` was `NA`, setting to ",
+        "the latest non-`NA` time value for these columns, {max_time}."
       ),
       class = "epipredict__get_forecast_date__max_time_warning"
     )
     forecast_date <- max_time
-  } else if (forecast_date < max_time) {
+  } else if (!is.null(max_time) && (forecast_date < max_time)) {
     cli_abort(
       paste(
         "`forecast_date` ({(forecast_date)}) is before the most ",
@@ -101,22 +92,49 @@ get_forecast_date <- function(new_data, info, epi_keys_checked, latency, columns
     )
   }
   # TODO cover the rest of the possible types for as_of and max_time...
-  if (inherits(max_time, "Date")) {
+  if (inherits(new_data$time_value, "Date")) {
     forecast_date <- as.Date(forecast_date)
   }
   return(forecast_date)
 }
 
+get_max_time <- function(new_data, epi_keys_checked, columns) {
+  # these are the non-na time_values;
+  # get the minimum value across the checked epi_keys' maximum time values
+  max_time <- new_data %>%
+    select(all_of(columns)) %>%
+    drop_na()
+  if (nrow(max_time) == 0) {
+    return(NULL)
+  }
+  # null and "" don't work in `group_by`
+  if (!is.null(epi_keys_checked) && all(epi_keys_checked != "")) {
+    max_time <- max_time %>% group_by(across(all_of(epi_keys_checked)))
+  }
+  max_time <- max_time %>%
+    summarise(time_value = max(time_value)) %>%
+    pull(time_value) %>%
+    min()
+  return(max_time)
+}
+
+
+
 #' the latency is also the amount the shift is off by
 #' @param sign_shift integer. 1 if lag and -1 if ahead. These represent how you
 #'   need to shift the data to bring the 3 day lagged value to today.
 #' @keywords internal
 get_latency <- function(new_data, forecast_date, column, sign_shift, epi_keys_checked) {
   shift_max_date <- new_data %>%
     drop_na(all_of(column))
+  if (nrow(shift_max_date) == 0) {
+    # if everything is an NA, there's infinite latency, but shifting by that is
+    # untenable. May as well not shift at all
+    return(0)
+  }
   # null and "" don't work in `group_by`
-  if (!is.null(epi_keys_checked) && epi_keys_checked != "") {
-    shift_max_date <- shift_max_date %>% group_by(get(epi_keys_checked))
+  if (!is.null(epi_keys_checked) && all(epi_keys_checked != "")) {
+    shift_max_date <- shift_max_date %>% group_by(across(all_of(epi_keys_checked)))
   }
   shift_max_date <- shift_max_date %>%
     summarise(time_value = max(time_value)) %>%
@@ -290,7 +308,8 @@ check_interminable_latency <- function(dataset, latency_table, target_columns, f
 #' @keywords internal
 #' @importFrom dplyr rowwise
 get_latency_table <- function(training, columns, forecast_date, latency,
-                              sign_shift, epi_keys_checked, info, terms) {
+                              sign_shift, epi_keys_checked, keys_to_ignore,
+                              info, terms) {
   if (is.null(columns)) {
     columns <- recipes_eval_select(terms, training, info)
   }
@@ -300,12 +319,17 @@ get_latency_table <- function(training, columns, forecast_date, latency,
   if (length(columns) > 0) {
     latency_table <- latency_table %>% filter(col_name %in% columns)
   }
-
+  training_dropped <- training %>%
+    drop_ignored_keys(keys_to_ignore)
   if (is.null(latency)) {
     latency_table <- latency_table %>%
       rowwise() %>%
       mutate(latency = get_latency(
-        training, forecast_date, col_name, sign_shift, epi_keys_checked
+        training_dropped,
+        forecast_date,
+        col_name,
+        sign_shift,
+        epi_keys_checked
       ))
   } else if (length(latency) > 1) {
     # if latency has a length, it must also have named elements.
@@ -319,7 +343,7 @@ get_latency_table <- function(training, columns, forecast_date, latency,
     latency_table <- latency_table %>%
       rowwise() %>%
       mutate(latency = get_latency(
-        training, forecast_date, col_name, sign_shift, epi_keys_checked
+        training %>% drop_ignored_keys(keys_to_ignore), forecast_date, col_name, sign_shift, epi_keys_checked
       ))
     if (latency) {
       latency_table <- latency_table %>% mutate(latency = latency)
@@ -328,6 +352,19 @@ get_latency_table <- function(training, columns, forecast_date, latency,
   return(latency_table %>% ungroup())
 }
 
+#' given a list named by key columns, remove any matching key values
+#' keys_to_ignore should have the form list(col_name = c("value_to_ignore", "other_value_to_ignore"))
+#' @keywords internal
+drop_ignored_keys <- function(training, keys_to_ignore) {
+  # note that the extra parenthesis black magic is described here: https://github.com/tidyverse/dplyr/issues/6194
+  # and is needed to bypass an incomplete port of `across` functions to `if_any`
+  training %>%
+    filter((dplyr::if_all(
+      names(keys_to_ignore),
+      ~ . %nin% keys_to_ignore[[cur_column()]]
+    )))
+}
+
 
 #' checks: the recipe type, whether a previous step is the relevant epi_shift,
 #' that either `fixed_latency` or `fixed_forecast_date` is non-null, and that
@@ -394,7 +431,7 @@ compare_bake_prep_latencies <- function(object, new_data, call = caller_env()) {
     )
   local_latency_table <- get_latency_table(
     new_data, object$columns, current_forecast_date, latency,
-    get_sign(object), object$epi_keys_checked, NULL, NULL
+    get_sign(object), object$epi_keys_checked, object$keys_to_ignore, NULL, NULL
   )
   comparison_table <- local_latency_table %>%
     ungroup() %>%