Deprecate epi_recipe() in favour of recipe()

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.0.20
+Version: 0.1.0
 Authors@R: c(
     person("Daniel", "McDonald", , "[email protected]", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "[email protected]", role = "aut"),
@@ -23,7 +23,7 @@ URL: https://github.com/cmu-delphi/epipredict/,
     https://cmu-delphi.github.io/epipredict
 BugReports: https://github.com/cmu-delphi/epipredict/issues/
 Depends:
-    epiprocess (>= 0.7.5),
+    epiprocess (>= 0.7.12),
     parsnip (>= 1.0.0),
     R (>= 3.5.0)
 Imports:

NAMESPACE

@@ -29,8 +29,6 @@ S3method(bake,step_training_window)
 S3method(detect_layer,frosting)
 S3method(detect_layer,workflow)
 S3method(epi_recipe,default)
-S3method(epi_recipe,epi_df)
-S3method(epi_recipe,formula)
 S3method(extract_argument,epi_workflow)
 S3method(extract_argument,frosting)
 S3method(extract_argument,layer)
@@ -96,6 +94,8 @@ S3method(print,step_naomit)
 S3method(print,step_population_scaling)
 S3method(print,step_training_window)
 S3method(quantile,dist_quantiles)
+S3method(recipe,epi_df)
+S3method(recipes::recipe,formula)
 S3method(refresh_blueprint,default_epi_recipe_blueprint)
 S3method(residuals,flatline)
 S3method(run_mold,default_epi_recipe_blueprint)
@@ -152,7 +152,6 @@ export(default_epi_recipe_blueprint)
 export(detect_layer)
 export(dist_quantiles)
 export(epi_recipe)
-export(epi_recipe_blueprint)
 export(epi_workflow)
 export(extract_argument)
 export(extract_frosting)
@@ -183,13 +182,12 @@ export(layer_residual_quantiles)
 export(layer_threshold)
 export(layer_unnest)
 export(nested_quantiles)
-export(new_default_epi_recipe_blueprint)
-export(new_epi_recipe_blueprint)
 export(pivot_quantiles_longer)
 export(pivot_quantiles_wider)
 export(prep)
 export(quantile_reg)
 export(rand_id)
+export(recipe)
 export(remove_epi_recipe)
 export(remove_frosting)
 export(remove_model)
@@ -264,6 +262,7 @@ importFrom(magrittr,"%>%")
 importFrom(recipes,bake)
 importFrom(recipes,prep)
 importFrom(recipes,rand_id)
+importFrom(recipes,recipe)
 importFrom(rlang,"!!!")
 importFrom(rlang,"!!")
 importFrom(rlang,"%@%")

R/arx_classifier.R

@@ -10,7 +10,7 @@
 #'   be real-valued. Conversion of this data to unordered classes is handled
 #'   internally based on the `breaks` argument to [arx_class_args_list()].
 #'   If discrete classes are already in the `epi_df`, it is recommended to
-#'   code up a classifier from scratch using [epi_recipe()].
+#'   code up a classifier from scratch using [recipe()].
 #' @param trainer A `{parsnip}` model describing the type of estimation.
 #'   For now, we enforce `mode = "classification"`. Typical values are
 #'   [parsnip::logistic_reg()] or [parsnip::multinom_reg()]. More complicated
@@ -129,7 +129,7 @@ arx_class_epi_workflow <- function(
 
   # --- preprocessor
   # ------- predictors
-  r <- epi_recipe(epi_data) %>%
+  r <- recipe(epi_data) %>%
     step_growth_rate(
       dplyr::all_of(predictors),
       role = "grp",

R/arx_forecaster.R

@@ -122,7 +122,7 @@ arx_fcast_epi_workflow <- function(
   lags <- arx_lags_validator(predictors, args_list$lags)
 
   # --- preprocessor
-  r <- epi_recipe(epi_data)
+  r <- recipe(epi_data)
   for (l in seq_along(lags)) {
     p <- predictors[l]
     r <- step_epi_lag(r, !!p, lag = lags[[l]])

R/autoplot.R

@@ -32,7 +32,7 @@ ggplot2::autoplot
 #' jhu <- case_death_rate_subset %>%
 #'   filter(time_value >= as.Date("2021-11-01"))
 #'
-#' r <- epi_recipe(jhu) %>%
+#' r <- recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
 #'   step_epi_ahead(death_rate, ahead = 7) %>%
 #'   step_epi_lag(case_rate, lag = c(0, 7, 14)) %>%
@@ -56,7 +56,7 @@ ggplot2::autoplot
 #' # ------- Show multiple horizons
 #'
 #' p <- lapply(c(7, 14, 21, 28), function(h) {
-#'   r <- epi_recipe(jhu) %>%
+#'   r <- recipe(jhu) %>%
 #'     step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
 #'     step_epi_ahead(death_rate, ahead = h) %>%
 #'     step_epi_lag(case_rate, lag = c(0, 7, 14)) %>%
@@ -184,7 +184,10 @@ autoplot.epi_workflow <- function(
   }
 
   if (".pred" %in% names(predictions)) {
-    ntarget_dates <- n_distinct(predictions$time_value)
+    ntarget_dates <- dplyr::n_distinct(predictions$time_value)
+    if (distributional::is_distribution(predictions$.pred)) {
+      predictions <- dplyr::mutate(predictions, .pred = median(.pred))
+    }
     if (ntarget_dates > 1L) {
       bp <- bp +
         geom_line(

R/blueprint-epi_recipe-default.R

@@ -1,111 +1,69 @@
-#' Recipe blueprint that accounts for `epi_df` panel data
-#'
-#' Used for simplicity. See [hardhat::new_recipe_blueprint()] or
-#' [hardhat::default_recipe_blueprint()] for more details.
-#'
-#' @inheritParams hardhat::new_recipe_blueprint
+#' Default epi_recipe blueprint
 #'
-#' @details The `bake_dependent_roles` are automatically set to `epi_df` defaults.
-#' @return A recipe blueprint.
+#' Recipe blueprint that accounts for `epi_df` panel data
+#' Used for simplicity. See [hardhat::default_recipe_blueprint()] for more
+#' details. This subclass is nearly the same, except it ensures that
+#' downstream processing doesn't drop the epi_df class from the data.
 #'
-#' @keywords internal
+#' @inheritParams hardhat::default_recipe_blueprint
+#' @return A `epi_recipe` blueprint.
 #' @export
-new_epi_recipe_blueprint <-
-  function(intercept = FALSE, allow_novel_levels = FALSE, fresh = TRUE,
-           composition = "tibble",
-           ptypes = NULL, recipe = NULL, ..., subclass = character()) {
-    hardhat::new_recipe_blueprint(
-      intercept = intercept,
-      allow_novel_levels = allow_novel_levels,
-      fresh = fresh,
-      composition = composition,
-      ptypes = ptypes,
-      recipe = recipe,
-      ...,
-      subclass = c(subclass, "epi_recipe_blueprint")
-    )
-  }
-
-
-#' @rdname new_epi_recipe_blueprint
-#' @export
-epi_recipe_blueprint <-
-  function(intercept = FALSE, allow_novel_levels = FALSE,
-           fresh = TRUE,
-           composition = "tibble") {
-    new_epi_recipe_blueprint(
-      intercept = intercept,
-      allow_novel_levels = allow_novel_levels,
-      fresh = fresh,
-      composition = composition
-    )
-  }
+#' @keywords internal
+default_epi_recipe_blueprint <- function(intercept = FALSE,
+                                         allow_novel_levels = FALSE,
+                                         fresh = TRUE,
+                                         strings_as_factors = FALSE,
+                                         composition = "tibble") {
+  new_default_epi_recipe_blueprint(
+    intercept = intercept,
+    allow_novel_levels = allow_novel_levels,
+    fresh = fresh,
+    strings_as_factors = strings_as_factors,
+    composition = composition
+  )
+}
 
-#' @rdname new_epi_recipe_blueprint
-#' @export
-default_epi_recipe_blueprint <-
-  function(intercept = FALSE, allow_novel_levels = FALSE, fresh = TRUE,
-           composition = "tibble") {
-    new_default_epi_recipe_blueprint(
-      intercept = intercept,
-      allow_novel_levels = allow_novel_levels,
-      fresh = fresh,
-      composition = composition
-    )
-  }
+new_default_epi_recipe_blueprint <- function(intercept = FALSE,
+                                             allow_novel_levels = TRUE,
+                                             fresh = TRUE,
+                                             strings_as_factors = FALSE,
+                                             composition = "tibble",
+                                             ptypes = NULL,
+                                             recipe = NULL,
+                                             extra_role_ptypes = NULL,
+                                             ...,
+                                             subclass = character()) {
+  hardhat::new_recipe_blueprint(
+    intercept = intercept,
+    allow_novel_levels = allow_novel_levels,
+    fresh = fresh,
+    strings_as_factors = strings_as_factors,
+    composition = composition,
+    ptypes = ptypes,
+    recipe = recipe,
+    extra_role_ptypes = extra_role_ptypes,
+    ...,
+    subclass = c(subclass, "default_epi_recipe_blueprint", "default_recipe_blueprint")
+  )
+}
 
-#' @rdname new_epi_recipe_blueprint
-#' @inheritParams hardhat::new_default_recipe_blueprint
-#' @export
-new_default_epi_recipe_blueprint <-
-  function(intercept = FALSE, allow_novel_levels = FALSE,
-           fresh = TRUE,
-           composition = "tibble", ptypes = NULL, recipe = NULL,
-           extra_role_ptypes = NULL, ..., subclass = character()) {
-    new_epi_recipe_blueprint(
-      intercept = intercept,
-      allow_novel_levels = allow_novel_levels,
-      fresh = fresh,
-      composition = composition,
-      ptypes = ptypes,
-      recipe = recipe,
-      extra_role_ptypes = extra_role_ptypes,
-      ...,
-      subclass = c(subclass, "default_epi_recipe_blueprint", "default_recipe_blueprint")
-    )
-  }
 
 #' @importFrom hardhat run_mold
 #' @export
 run_mold.default_epi_recipe_blueprint <- function(blueprint, ..., data) {
   rlang::check_dots_empty0(...)
-  # blueprint <- hardhat:::patch_recipe_default_blueprint(blueprint)
-  cleaned <- mold_epi_recipe_default_clean(blueprint = blueprint, data = data)
-  blueprint <- cleaned$blueprint
-  data <- cleaned$data
+  # we don't do the "cleaning" in `hardhat:::run_mold.default_recipe_blueprint`
+  # That function drops the epi_df class without any recourse.
+  # The only way we should be here at all is if `data` is an epi_df, but just
+  # in case...
+  if (!is_epi_df(data)) {
+    cli_warn("`data` is not an {.cls epi_df}. It has class {.cls {class(data)}}.")
+  }
   hardhat:::mold_recipe_default_process(blueprint = blueprint, data = data)
 }
 
-mold_epi_recipe_default_clean <- function(blueprint, data) {
-  hardhat:::check_data_frame_or_matrix(data)
-  if (!is_epi_df(data)) data <- hardhat:::coerce_to_tibble(data)
-  hardhat:::new_mold_clean(blueprint, data)
-}
-
 #' @importFrom hardhat refresh_blueprint
 #' @export
 refresh_blueprint.default_epi_recipe_blueprint <- function(blueprint) {
   do.call(new_default_epi_recipe_blueprint, as.list(blueprint))
 }
-
-
-## removing this function?
-# er_check_is_data_like <- function(.x, .x_nm) {
-#   if (rlang::is_missing(.x_nm)) {
-#     .x_nm <- rlang::as_label(rlang::enexpr(.x))
-#   }
-#   if (!hardhat:::is_new_data_like(.x)) {
-#     hardhat:::glubort("`{.x_nm}` must be a data.frame or a matrix, not a {class1(.x)}.")
-#   }
-#   .x
-# }

R/cdc_baseline_forecaster.R

@@ -36,25 +36,25 @@
 #' cdc <- cdc_baseline_forecaster(weekly_deaths, "deaths")
 #' preds <- pivot_quantiles_wider(cdc$predictions, .pred_distn)
 #'
-#' if (require(ggplot2)) {
-#'   forecast_date <- unique(preds$forecast_date)
-#'   four_states <- c("ca", "pa", "wa", "ny")
-#'   preds %>%
-#'     filter(geo_value %in% four_states) %>%
-#'     ggplot(aes(target_date)) +
-#'     geom_ribbon(aes(ymin = `0.1`, ymax = `0.9`), fill = blues9[3]) +
-#'     geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
-#'     geom_line(aes(y = .pred), color = "orange") +
-#'     geom_line(
-#'       data = weekly_deaths %>% filter(geo_value %in% four_states),
-#'       aes(x = time_value, y = deaths)
-#'     ) +
-#'     scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
-#'     labs(x = "Date", y = "Weekly deaths") +
-#'     facet_wrap(~geo_value, scales = "free_y") +
-#'     theme_bw() +
-#'     geom_vline(xintercept = forecast_date)
-#' }
+#' library(ggplot2)
+#' forecast_date <- unique(preds$forecast_date)
+#' four_states <- c("ca", "pa", "wa", "ny")
+#' preds %>%
+#'   filter(geo_value %in% four_states) %>%
+#'   ggplot(aes(target_date)) +
+#'   geom_ribbon(aes(ymin = `0.1`, ymax = `0.9`), fill = blues9[3]) +
+#'   geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
+#'   geom_line(aes(y = .pred), color = "orange") +
+#'   geom_line(
+#'     data = weekly_deaths %>% filter(geo_value %in% four_states),
+#'     aes(x = time_value, y = deaths)
+#'   ) +
+#'   scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
+#'   labs(x = "Date", y = "Weekly deaths") +
+#'   facet_wrap(~geo_value, scales = "free_y") +
+#'   theme_bw() +
+#'   geom_vline(xintercept = forecast_date)
+#'
 cdc_baseline_forecaster <- function(
     epi_data,
     outcome,
@@ -68,7 +68,7 @@ cdc_baseline_forecaster <- function(
   outcome <- rlang::sym(outcome)
 
 
-  r <- epi_recipe(epi_data) %>%
+  r <- recipe(epi_data) %>%
     step_epi_ahead(!!outcome, ahead = args_list$data_frequency, skip = TRUE) %>%
     recipes::update_role(!!outcome, new_role = "predictor") %>%
     recipes::add_role(tidyselect::all_of(keys), new_role = "predictor") %>%
@@ -79,7 +79,7 @@ cdc_baseline_forecaster <- function(
 
 
   latest <- get_test_data(
-    epi_recipe(epi_data), epi_data, TRUE, args_list$nafill_buffer,
+    recipe(epi_data), epi_data, TRUE, args_list$nafill_buffer,
     forecast_date
   )