refactor: replace predict with forecast in vignettes, tests, examples

Author: dshemetov

R/arx_classifier.R

@@ -54,14 +54,14 @@ arx_classifier <- function(
   wf <- arx_class_epi_workflow(
     epi_data, outcome, predictors, trainer, args_list
   )
-
-  latest <- get_test_data(
-    hardhat::extract_preprocessor(wf), epi_data, TRUE, args_list$nafill_buffer,
-    args_list$forecast_date %||% max(epi_data$time_value)
-  )
-
   wf <- generics::fit(wf, epi_data)
-  preds <- predict(wf, new_data = latest) %>%
+
+  preds <- forecast(
+    wf,
+    fill_locf = TRUE,
+    n_recent = args_list$nafill_buffer,
+    forecast_date = args_list$forecast_date %||% max(epi_data$time_value)
+  ) %>%
     tibble::as_tibble() %>%
     dplyr::select(-time_value)
 

R/arx_forecaster.R

@@ -50,14 +50,18 @@ arx_forecaster <- function(epi_data,
   wf <- arx_fcast_epi_workflow(
     epi_data, outcome, predictors, trainer, args_list
   )
+  wf <- generics::fit(wf, epi_data)
 
   latest <- get_test_data(
-    hardhat::extract_preprocessor(wf), epi_data, TRUE, args_list$nafill_buffer,
-    args_list$forecast_date %||% max(epi_data$time_value)
+    hardhat::extract_preprocessor(wf), epi_data,
   )
 
-  wf <- generics::fit(wf, epi_data)
-  preds <- predict(wf, new_data = latest) %>%
+  preds <- forecast(
+    wf,
+    fill_locf = TRUE,
+    n_recent = args_list$nafill_buffer,
+    forecast_date = args_list$forecast_date %||% max(epi_data$time_value)
+  ) %>%
     tibble::as_tibble() %>%
     dplyr::select(-time_value)
 

R/autoplot.R

@@ -61,8 +61,7 @@ ggplot2::autoplot
 #'     step_epi_lag(case_rate, lag = c(0, 7, 14)) %>%
 #'     step_epi_naomit()
 #'   ewf <- epi_workflow(r, parsnip::linear_reg(), f) %>% fit(jhu)
-#'   td <- get_test_data(r, jhu)
-#'   predict(ewf, new_data = td)
+#'   forecast(ewf)
 #' })
 #'
 #' p <- do.call(rbind, p)

R/flatline_forecaster.R

@@ -49,11 +49,6 @@ flatline_forecaster <- function(
   forecast_date <- args_list$forecast_date %||% max(epi_data$time_value)
   target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
 
-  latest <- get_test_data(
-    epi_recipe(epi_data), epi_data, TRUE, args_list$nafill_buffer,
-    forecast_date
-  )
-
   f <- frosting() %>%
     layer_predict() %>%
     layer_residual_quantiles(
@@ -69,7 +64,12 @@ flatline_forecaster <- function(
 
   wf <- epi_workflow(r, eng, f)
   wf <- generics::fit(wf, epi_data)
-  preds <- suppressWarnings(predict(wf, new_data = latest)) %>%
+  preds <- suppressWarnings(forecast(
+    wf,
+    fill_locf = TRUE,
+    n_recent = args_list$nafill_buffer,
+    forecast_date = forecast_date
+  )) %>%
     tibble::as_tibble() %>%
     dplyr::select(-time_value)
 

R/frosting.R

@@ -275,15 +275,14 @@ new_frosting <- function() {
 #'   step_epi_naomit()
 #'
 #' wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu)
-#' latest <- get_test_data(recipe = r, x = jhu)
 #'
 #' f <- frosting() %>%
 #'   layer_predict() %>%
 #'   layer_naomit(.pred)
 #'
 #' wf1 <- wf %>% add_frosting(f)
 #'
-#' p <- predict(wf1, latest)
+#' p <- forecast(wf1)
 #' p
 frosting <- function(layers = NULL, requirements = NULL) {
   if (!is_null(layers) || !is_null(requirements)) {

R/layer_add_target_date.R

@@ -28,7 +28,6 @@
 #'   step_epi_naomit()
 #'
 #' wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu)
-#' latest <- get_test_data(r, jhu)
 #'
 #' # Use ahead + forecast date
 #' f <- frosting() %>%
@@ -38,7 +37,7 @@
 #'   layer_naomit(.pred)
 #' wf1 <- wf %>% add_frosting(f)
 #'
-#' p <- predict(wf1, latest)
+#' p <- forecast(wf1)
 #' p
 #'
 #' # Use ahead + max time value from pre, fit, post
@@ -49,7 +48,7 @@
 #'   layer_naomit(.pred)
 #' wf2 <- wf %>% add_frosting(f2)
 #'
-#' p2 <- predict(wf2, latest)
+#' p2 <- forecast(wf2)
 #' p2
 #'
 #' # Specify own target date
@@ -59,7 +58,7 @@
 #'   layer_naomit(.pred)
 #' wf3 <- wf %>% add_frosting(f3)
 #'
-#' p3 <- predict(wf3, latest)
+#' p3 <- forecast(wf3)
 #' p3
 layer_add_target_date <-
   function(frosting, target_date = NULL, id = rand_id("add_target_date")) {

R/layer_cdc_flatline_quantiles.R

@@ -64,18 +64,14 @@
 #'
 #' forecast_date <- max(case_death_rate_subset$time_value)
 #'
-#' latest <- get_test_data(
-#'   epi_recipe(case_death_rate_subset), case_death_rate_subset
-#' )
-#'
 #' f <- frosting() %>%
 #'   layer_predict() %>%
 #'   layer_cdc_flatline_quantiles(aheads = c(7, 14, 21, 28), symmetrize = TRUE)
 #'
 #' eng <- parsnip::linear_reg() %>% parsnip::set_engine("flatline")
 #'
 #' wf <- epi_workflow(r, eng, f) %>% fit(case_death_rate_subset)
-#' preds <- suppressWarnings(predict(wf, new_data = latest)) %>%
+#' preds <- suppressWarnings(forecast(wf)) %>%
 #'   dplyr::select(-time_value) %>%
 #'   dplyr::mutate(forecast_date = forecast_date)
 #' preds

R/layer_naomit.R

@@ -20,15 +20,13 @@
 #'
 #' wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu)
 #'
-#' latest <- get_test_data(recipe = r, x = jhu)
-#'
 #' f <- frosting() %>%
 #'   layer_predict() %>%
 #'   layer_naomit(.pred)
 #'
 #' wf1 <- wf %>% add_frosting(f)
 #'
-#' p <- predict(wf1, latest)
+#' p <- forecast(wf1)
 #' p
 layer_naomit <- function(frosting, ..., id = rand_id("naomit")) {
   arg_is_chr_scalar(id)

R/layer_point_from_distn.R

@@ -26,16 +26,14 @@
 #'
 #' wf <- epi_workflow(r, quantile_reg(quantile_levels = c(.25, .5, .75))) %>% fit(jhu)
 #'
-#' latest <- get_test_data(recipe = r, x = jhu)
-#'
 #' f1 <- frosting() %>%
 #'   layer_predict() %>%
 #'   layer_quantile_distn() %>% # puts the other quantiles in a different col
 #'   layer_point_from_distn() %>% # mutate `.pred` to contain only a point prediction
 #'   layer_naomit(.pred)
 #' wf1 <- wf %>% add_frosting(f1)
 #'
-#' p1 <- predict(wf1, latest)
+#' p1 <- forecast(wf1)
 #' p1
 #'
 #' f2 <- frosting() %>%
@@ -44,7 +42,7 @@
 #'   layer_naomit(.pred)
 #' wf2 <- wf %>% add_frosting(f2)
 #'
-#' p2 <- predict(wf2, latest)
+#' p2 <- forecast(wf2)
 #' p2
 layer_point_from_distn <- function(frosting,
                                    ...,

R/layer_population_scaling.R

@@ -78,17 +78,7 @@
 #'   fit(jhu) %>%
 #'   add_frosting(f)
 #'
-#' latest <- get_test_data(
-#'   recipe = r,
-#'   x = epiprocess::jhu_csse_daily_subset %>%
-#'     dplyr::filter(
-#'       time_value > "2021-11-01",
-#'       geo_value %in% c("ca", "ny")
-#'     ) %>%
-#'     dplyr::select(geo_value, time_value, cases)
-#' )
-#'
-#' predict(wf, latest)
+#' forecast(wf)
 layer_population_scaling <- function(frosting,
                                      ...,
                                      df,

R/layer_predictive_distn.R

@@ -30,15 +30,13 @@
 #'
 #' wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu)
 #'
-#' latest <- get_test_data(recipe = r, x = jhu)
-#'
 #' f <- frosting() %>%
 #'   layer_predict() %>%
 #'   layer_predictive_distn() %>%
 #'   layer_naomit(.pred)
 #' wf1 <- wf %>% add_frosting(f)
 #'
-#' p <- predict(wf1, latest)
+#' p <- forecast(wf1)
 #' p
 layer_predictive_distn <- function(frosting,
                                    ...,

R/layer_quantile_distn.R

@@ -28,15 +28,13 @@
 #' wf <- epi_workflow(r, quantile_reg(quantile_levels = c(.25, .5, .75))) %>%
 #'   fit(jhu)
 #'
-#' latest <- get_test_data(recipe = r, x = jhu)
-#'
 #' f <- frosting() %>%
 #'   layer_predict() %>%
 #'   layer_quantile_distn() %>%
 #'   layer_naomit(.pred)
 #' wf1 <- wf %>% add_frosting(f)
 #'
-#' p <- predict(wf1, latest)
+#' p <- forecast(wf1)
 #' p
 layer_quantile_distn <- function(frosting,
                                  ...,

R/layer_residual_quantiles.R

@@ -24,23 +24,21 @@
 #'
 #' wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu)
 #'
-#' latest <- get_test_data(recipe = r, x = jhu)
-#'
 #' f <- frosting() %>%
 #'   layer_predict() %>%
 #'   layer_residual_quantiles(quantile_levels = c(0.0275, 0.975), symmetrize = FALSE) %>%
 #'   layer_naomit(.pred)
 #' wf1 <- wf %>% add_frosting(f)
 #'
-#' p <- predict(wf1, latest)
+#' p <- forecast(wf1)
 #'
 #' f2 <- frosting() %>%
 #'   layer_predict() %>%
 #'   layer_residual_quantiles(quantile_levels = c(0.3, 0.7), by_key = "geo_value") %>%
 #'   layer_naomit(.pred)
 #' wf2 <- wf %>% add_frosting(f2)
 #'
-#' p2 <- predict(wf2, latest)
+#' p2 <- forecast(wf2)
 layer_residual_quantiles <- function(
     frosting, ...,
     quantile_levels = c(0.05, 0.95),

R/layer_threshold_preds.R

@@ -32,13 +32,11 @@
 #'   step_epi_naomit()
 #' wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu)
 #'
-#' latest <- get_test_data(r, jhu)
-#'
 #' f <- frosting() %>%
 #'   layer_predict() %>%
 #'   layer_threshold(.pred, lower = 0.180, upper = 0.310)
 #' wf <- wf %>% add_frosting(f)
-#' p <- predict(wf, latest)
+#' p <- forecast(wf)
 #' p
 layer_threshold <-
   function(frosting, ..., lower = 0, upper = Inf, id = rand_id("threshold")) {

R/step_population_scaling.R

@@ -96,18 +96,7 @@
 #'   fit(jhu) %>%
 #'   add_frosting(f)
 #'
-#' latest <- get_test_data(
-#'   recipe = r,
-#'   epiprocess::jhu_csse_daily_subset %>%
-#'     dplyr::filter(
-#'       time_value > "2021-11-01",
-#'       geo_value %in% c("ca", "ny")
-#'     ) %>%
-#'     dplyr::select(geo_value, time_value, cases)
-#' )
-#'
-#'
-#' predict(wf, latest)
+#' forecast(wf)
 step_population_scaling <-
   function(recipe,
            ...,