Make layer_predict forward stored dots_list to predict()

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.0.16
+Version: 0.0.17
 Authors@R: c(
     person("Daniel", "McDonald", , "[email protected]", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "[email protected]", role = "aut"),

NAMESPACE

@@ -208,6 +208,7 @@ import(parsnip)
 import(recipes)
 importFrom(checkmate,assert)
 importFrom(checkmate,assert_character)
+importFrom(checkmate,assert_class)
 importFrom(checkmate,assert_date)
 importFrom(checkmate,assert_function)
 importFrom(checkmate,assert_int)

NEWS.md

@@ -47,3 +47,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Revise `compat-purrr` to use the r-lang `standalone-*` version (via
   `{usethis}`)
 - `epi_recipe()` will now warn when given non-`epi_df` data
+- `layer_predict()` and `predict.epi_workflow()` will now appropriately forward
+  `...` args intended for `predict.model_fit()`
+- `bake.epi_recipe()` will now re-infer the geo and time type in case baking the
+  steps has changed the appropriate values

R/arx_classifier.R

@@ -1,7 +1,7 @@
 #' Direct autoregressive classifier with covariates
 #'
 #' This is an autoregressive classification model for
-#' [epiprocess::epi_df] data. It does "direct" forecasting, meaning
+#' [epiprocess::epi_df][epiprocess::as_epi_df] data. It does "direct" forecasting, meaning
 #' that it estimates a class at a particular target horizon.
 #'
 #' @inheritParams arx_forecaster

R/arx_forecaster.R

@@ -1,7 +1,7 @@
 #' Direct autoregressive forecaster with covariates
 #'
 #' This is an autoregressive forecasting model for
-#' [epiprocess::epi_df] data. It does "direct" forecasting, meaning
+#' [epiprocess::epi_df][epiprocess::as_epi_df] data. It does "direct" forecasting, meaning
 #' that it estimates a model for a particular target horizon.
 #'
 #'

R/cdc_baseline_forecaster.R

@@ -1,7 +1,7 @@
 #' Predict the future with the most recent value
 #'
 #' This is a simple forecasting model for
-#' [epiprocess::epi_df] data. It uses the most recent observation as the
+#' [epiprocess::epi_df][epiprocess::as_epi_df] data. It uses the most recent observation as the
 #' forecast for any future date, and produces intervals by shuffling the quantiles
 #' of the residuals of such a "flatline" forecast and incrementing these
 #' forward over all available training data.
@@ -12,7 +12,7 @@
 #' This forecaster is meant to produce exactly the CDC Baseline used for
 #' [COVID19ForecastHub](https://covid19forecasthub.org)
 #'
-#' @param epi_data An [`epiprocess::epi_df`]
+#' @param epi_data An [`epiprocess::epi_df`][epiprocess::as_epi_df]
 #' @param outcome A scalar character for the column name we wish to predict.
 #' @param args_list A list of additional arguments as created by the
 #'   [cdc_baseline_args_list()] constructor function.

R/data.R

@@ -59,7 +59,7 @@
 
 #' Subset of Statistics Canada median employment income for postsecondary graduates
 #'
-#' @format An [epiprocess::epi_df] with 10193 rows and 8 variables:
+#' @format An [epiprocess::epi_df][epiprocess::as_epi_df] with 10193 rows and 8 variables:
 #' \describe{
 #'   \item{geo_value}{The province in Canada associated with each
 #'      row of measurements.}

R/epi_recipe.R

@@ -245,7 +245,7 @@ is_epi_recipe <- function(x) {
 #' @details
 #' `add_epi_recipe` has the same behaviour as
 #' [workflows::add_recipe()] but sets a different
-#' default blueprint to automatically handle [epiprocess::epi_df] data.
+#' default blueprint to automatically handle [epiprocess::epi_df][epiprocess::as_epi_df] data.
 #'
 #' @param x A `workflow` or `epi_workflow`
 #'
@@ -572,9 +572,13 @@ bake.epi_recipe <- function(object, new_data, ..., composition = "epi_df") {
   }
   new_data <- NextMethod("bake")
   if (!is.null(meta)) {
+    # Baking should have dropped epi_df-ness and metadata. Re-infer some
+    # metadata and assume others remain the same as the object/template:
     new_data <- as_epi_df(
-      new_data, meta$geo_type, meta$time_type, meta$as_of,
-      meta$additional_metadata %||% list()
+      new_data,
+      as_of = meta$as_of,
+      # avoid NULL if meta is from saved older epi_df:
+      additional_metadata = meta$additional_metadata %||% list()
     )
   }
   new_data

R/epi_workflow.R

@@ -119,18 +119,18 @@ fit.epi_workflow <- function(object, data, ..., control = workflows::control_wor
 #' - Call [parsnip::predict.model_fit()] for you using the underlying fit
 #'   parsnip model.
 #'
-#' - Ensure that the returned object is an [epiprocess::epi_df] where
+#' - Ensure that the returned object is an [epiprocess::epi_df][epiprocess::as_epi_df] where
 #'   possible. Specifically, the output will have `time_value` and
 #'   `geo_value` columns as well as the prediction.
 #'
-#' @inheritParams parsnip::predict.model_fit
-#'
 #' @param object An epi_workflow that has been fit by
 #'   [workflows::fit.workflow()]
 #'
 #' @param new_data A data frame containing the new predictors to preprocess
 #'   and predict on
 #'
+#' @inheritParams parsnip::predict.model_fit
+#'
 #' @return
 #' A data frame of model predictions, with as many rows as `new_data` has.
 #' If `new_data` is an `epi_df` or a data frame with `time_value` or
@@ -152,7 +152,7 @@ fit.epi_workflow <- function(object, data, ..., control = workflows::control_wor
 #'
 #' preds <- predict(wf, latest)
 #' preds
-predict.epi_workflow <- function(object, new_data, ...) {
+predict.epi_workflow <- function(object, new_data, type = NULL, opts = list(), ...) {
   if (!workflows::is_trained_workflow(object)) {
     cli::cli_abort(c(
       "Can't predict on an untrained epi_workflow.",
@@ -168,7 +168,7 @@ predict.epi_workflow <- function(object, new_data, ...) {
     components$forged,
     components$mold, new_data
   )
-  components <- apply_frosting(object, components, new_data, ...)
+  components <- apply_frosting(object, components, new_data, type = type, opts = opts, ...)
   components$predictions
 }
 

R/epipredict-package.R

@@ -6,7 +6,7 @@
 #' @importFrom cli cli_abort
 #' @importFrom checkmate assert assert_character assert_int assert_scalar
 #'             assert_logical assert_numeric assert_number assert_integer
-#'             assert_integerish assert_date assert_function
+#'             assert_integerish assert_date assert_function assert_class
 #' @import epiprocess parsnip
 ## usethis namespace: end
 NULL

R/flatline_forecaster.R

@@ -1,7 +1,7 @@
 #' Predict the future with today's value
 #'
 #' This is a simple forecasting model for
-#' [epiprocess::epi_df] data. It uses the most recent observation as the
+#' [epiprocess::epi_df][epiprocess::as_epi_df] data. It uses the most recent observation as the
 #' forcast for any future date, and produces intervals based on the quantiles
 #' of the residuals of such a "flatline" forecast over all available training
 #' data.
@@ -13,7 +13,7 @@
 #' This forecaster is very similar to that used by the
 #' [COVID19ForecastHub](https://covid19forecasthub.org)
 #'
-#' @param epi_data An [epiprocess::epi_df]
+#' @param epi_data An [epiprocess::epi_df][epiprocess::as_epi_df]
 #' @param outcome A scalar character for the column name we wish to predict.
 #' @param args_list A list of dditional arguments as created by the
 #'   [flatline_args_list()] constructor function.

R/frosting.R

@@ -355,9 +355,11 @@ apply_frosting.default <- function(workflow, components, ...) {
 #' @rdname apply_frosting
 #' @importFrom rlang is_null
 #' @importFrom rlang abort
+#' @param type,opts forwarded (along with `...`) to [`predict.model_fit()`] and
+#'   [`slather()`] for supported layers
 #' @export
 apply_frosting.epi_workflow <-
-  function(workflow, components, new_data, ...) {
+  function(workflow, components, new_data, type = NULL, opts = list(), ...) {
     the_fit <- workflows::extract_fit_parsnip(workflow)
 
     if (!has_postprocessor(workflow)) {
@@ -376,7 +378,7 @@ apply_frosting.epi_workflow <-
         "Returning unpostprocessed predictions."
       ))
       components$predictions <- predict(
-        the_fit, components$forged$predictors, ...
+        the_fit, components$forged$predictors, type, opts, ...
       )
       components$predictions <- dplyr::bind_cols(
         components$keys, components$predictions
@@ -397,10 +399,28 @@ apply_frosting.epi_workflow <-
         layers
       )
     }
+    if (length(layers) > 1L &&
+      (!is.null(type) || !identical(opts, list()) || rlang::dots_n(...) > 0L)) {
+      cli_abort("
+        Passing `type`, `opts`, or `...` into `predict.epi_workflow()` is not
+        supported if you have frosting layers other than `layer_predict`. Please
+        provide these arguments earlier (i.e. while constructing the frosting
+        object) by passing them into an explicit call to `layer_predict(), and
+        adjust the remaining layers to account for resulting differences in
+        output format from these settings.
+      ", class = "epipredict__apply_frosting__predict_settings_with_unsupported_layers")
+    }
 
     for (l in seq_along(layers)) {
       la <- layers[[l]]
-      components <- slather(la, components, workflow, new_data)
+      if (inherits(la, "layer_predict")) {
+        components <- slather(la, components, workflow, new_data, type = type, opts = opts, ...)
+      } else {
+        # The check above should ensure we have default `type` and `opts`, and
+        # empty `...`; don't forward these default `type` and `opts`, to avoid
+        # upsetting some slather method validation.
+        components <- slather(la, components, workflow, new_data)
+      }
     }
 
     return(components)

R/get_test_data.R

@@ -1,7 +1,7 @@
 #' Get test data for prediction based on longest lag period
 #'
 #' Based on the longest lag period in the recipe,
-#' `get_test_data()` creates an [epi_df]
+#' `get_test_data()` creates an [epi_df][epiprocess::as_epi_df]
 #' with columns `geo_value`, `time_value`
 #' and other variables in the original dataset,
 #' which will be used to create features necessary to produce forecasts.

R/layer_add_forecast_date.R

@@ -86,6 +86,7 @@ layer_add_forecast_date_new <- function(forecast_date, id) {
 
 #' @export
 slather.layer_add_forecast_date <- function(object, components, workflow, new_data, ...) {
+  rlang::check_dots_empty()
   if (is.null(object$forecast_date)) {
     max_time_value <- as.Date(max(
       workflows::extract_preprocessor(workflow)$max_time_value,

R/layer_naomit.R

@@ -45,6 +45,7 @@ layer_naomit_new <- function(terms, id) {
 
 #' @export
 slather.layer_naomit <- function(object, components, workflow, new_data, ...) {
+  rlang::check_dots_empty()
   exprs <- rlang::expr(c(!!!object$terms))
   pos <- tidyselect::eval_select(exprs, components$predictions)
   col_names <- names(pos)

R/layer_point_from_distn.R

@@ -76,7 +76,6 @@ layer_point_from_distn_new <- function(type, name, id) {
 #' @export
 slather.layer_point_from_distn <-
   function(object, components, workflow, new_data, ...) {
-    rlang::check_dots_empty()
     dstn <- components$predictions$.pred
     if (!inherits(dstn, "distribution")) {
       rlang::warn(
@@ -86,6 +85,7 @@ slather.layer_point_from_distn <-
       )
       return(components)
     }
+    rlang::check_dots_empty()
 
     dstn <- match.fun(object$type)(dstn)
     if (is.null(object$name)) {

R/layer_population_scaling.R

@@ -128,11 +128,11 @@ layer_population_scaling_new <-
 #' @export
 slather.layer_population_scaling <-
   function(object, components, workflow, new_data, ...) {
-    rlang::check_dots_empty()
     stopifnot(
       "Only one population column allowed for scaling" =
         length(object$df_pop_col) == 1
     )
+    rlang::check_dots_empty()
 
     if (is.null(object$by)) {
       object$by <- intersect(

R/layer_predict.R

@@ -45,12 +45,19 @@ layer_predict <-
            id = rand_id("predict_default")) {
     arg_is_chr_scalar(id)
     arg_is_chr_scalar(type, allow_null = TRUE)
+    assert_class(opts, "list")
+    dots_list <- rlang::dots_list(..., .homonyms = "error", .check_assign = TRUE)
+    if (any(rlang::names2(dots_list) == "")) {
+      cli_abort("All `...` arguments must be named.",
+        class = "epipredict__layer_predict__unnamed_dot"
+      )
+    }
     add_layer(
       frosting,
       layer_predict_new(
         type = type,
         opts = opts,
-        dots_list = rlang::list2(...), # can't figure how to use this
+        dots_list = dots_list,
         id = id
       )
     )
@@ -62,14 +69,27 @@ layer_predict_new <- function(type, opts, dots_list, id) {
 }
 
 #' @export
-slather.layer_predict <- function(object, components, workflow, new_data, ...) {
+slather.layer_predict <- function(object, components, workflow, new_data, type = NULL, opts = list(), ...) {
+  arg_is_chr_scalar(type, allow_null = TRUE)
+  if (!is.null(object$type) && !is.null(type) && !identical(object$type, type)) {
+    cli_abort("
+      Conflicting `type` settings were specified during frosting construction
+      (in call to `layer_predict()`) and while slathering (in call to
+      `slather()`/ `predict()`/etc.): {object$type} vs. {type}.  Please remove
+      one of these `type` settings.
+    ", class = "epipredict__layer_predict__conflicting_type_settings")
+  }
+  assert_class(opts, "list")
+
   the_fit <- workflows::extract_fit_parsnip(workflow)
 
-  components$predictions <- predict(
+  components$predictions <- rlang::inject(predict(
     the_fit,
     components$forged$predictors,
-    type = object$type, opts = object$opts
-  )
+    type = object$type %||% type,
+    opts = c(object$opts, opts),
+    !!!object$dots_list, ...
+  ))
   components$predictions <- dplyr::bind_cols(
     components$keys, components$predictions
   )

R/layer_predictive_distn.R

@@ -73,6 +73,7 @@ layer_predictive_distn_new <- function(dist_type, truncate, name, id) {
 slather.layer_predictive_distn <-
   function(object, components, workflow, new_data, ...) {
     the_fit <- workflows::extract_fit_parsnip(workflow)
+    rlang::check_dots_empty()
 
     m <- components$predictions$.pred
     r <- grab_residuals(the_fit, components)

R/layer_quantile_distn.R

@@ -79,6 +79,8 @@ slather.layer_quantile_distn <-
         "These are of class {.cls {class(dstn)}}."
       ))
     }
+    rlang::check_dots_empty()
+
     dstn <- dist_quantiles(
       quantile(dstn, object$quantile_levels),
       object$quantile_levels

R/layer_residual_quantiles.R

@@ -75,6 +75,8 @@ layer_residual_quantiles_new <- function(
 #' @export
 slather.layer_residual_quantiles <-
   function(object, components, workflow, new_data, ...) {
+    rlang::check_dots_empty()
+
     the_fit <- workflows::extract_fit_parsnip(workflow)
 
     if (is.null(object$quantile_levels)) {

R/layer_threshold_preds.R

@@ -98,6 +98,7 @@ snap.dist_quantiles <- function(x, lower, upper, ...) {
 #' @export
 slather.layer_threshold <-
   function(object, components, workflow, new_data, ...) {
+    rlang::check_dots_empty()
     exprs <- rlang::expr(c(!!!object$terms))
     pos <- tidyselect::eval_select(exprs, components$predictions)
     col_names <- names(pos)

R/layer_unnest.R

@@ -28,6 +28,7 @@ layer_unnest_new <- function(terms, id) {
 #' @export
 slather.layer_unnest <-
   function(object, components, workflow, new_data, ...) {
+    rlang::check_dots_empty()
     exprs <- rlang::expr(c(!!!object$terms))
     pos <- tidyselect::eval_select(exprs, components$predictions)
     col_names <- names(pos)

inst/templates/layer.R

@@ -29,6 +29,7 @@ layer_{{{ name }}}_new <- function(terms, args, more_args, id) {
 #' @export
 slather.layer_{{{ name }}} <-
   function(object, components, workflow, new_data, ...) {
+    rlang::check_dots_empty()
 
     # if layer_ used ... in tidyselect, we need to evaluate it now
     exprs <- rlang::expr(c(!!!object$terms))