feat: check_enough_train_data

.Rbuildignore

@@ -1,3 +1,5 @@
+^renv$
+^renv\.lock$
 ^epipredict\.Rproj$
 ^\.Rproj\.user$
 ^LICENSE\.md$

.gitignore

@@ -7,3 +7,6 @@ inst/doc
 .DS_Store
 /doc/
 /Meta/
+.Rprofile
+renv.lock
+renv/

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.0.7
+Version: 0.0.8
 Authors@R: c(
     person("Daniel", "McDonald", , "[email protected]", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "[email protected]", role = "aut"),
@@ -22,11 +22,11 @@ License: MIT + file LICENSE
 URL: https://github.com/cmu-delphi/epipredict/,
     https://cmu-delphi.github.io/epipredict
 BugReports: https://github.com/cmu-delphi/epipredict/issues/
-Depends: 
+Depends:
     epiprocess (>= 0.6.0),
     parsnip (>= 1.0.0),
     R (>= 3.5.0)
-Imports: 
+Imports:
     cli,
     distributional,
     dplyr,
@@ -48,7 +48,7 @@ Imports:
     usethis,
     vctrs,
     workflows (>= 1.0.0)
-Suggests: 
+Suggests:
     covidcast,
     data.table,
     epidatr (>= 1.0.0),
@@ -61,7 +61,7 @@ Suggests:
     rmarkdown,
     testthat (>= 3.0.0),
     xgboost
-VignetteBuilder: 
+VignetteBuilder:
     knitr
 Remotes:
     cmu-delphi/epidatr,

NAMESPACE

@@ -10,6 +10,7 @@ S3method(adjust_frosting,frosting)
 S3method(apply_frosting,default)
 S3method(apply_frosting,epi_workflow)
 S3method(augment,epi_workflow)
+S3method(bake,check_enough_train_data)
 S3method(bake,epi_recipe)
 S3method(bake,step_epi_ahead)
 S3method(bake,step_epi_lag)
@@ -48,6 +49,7 @@ S3method(mean,dist_quantiles)
 S3method(median,dist_quantiles)
 S3method(predict,epi_workflow)
 S3method(predict,flatline)
+S3method(prep,check_enough_train_data)
 S3method(prep,epi_recipe)
 S3method(prep,step_epi_ahead)
 S3method(prep,step_epi_lag)
@@ -60,6 +62,7 @@ S3method(print,arx_class)
 S3method(print,arx_fcast)
 S3method(print,canned_epipred)
 S3method(print,cdc_baseline_fcast)
+S3method(print,check_enough_train_data)
 S3method(print,epi_recipe)
 S3method(print,epi_workflow)
 S3method(print,flat_fcast)
@@ -104,6 +107,7 @@ S3method(snap,default)
 S3method(snap,dist_default)
 S3method(snap,dist_quantiles)
 S3method(snap,distribution)
+S3method(tidy,check_enough_train_data)
 S3method(tidy,frosting)
 S3method(tidy,layer)
 S3method(update,layer)
@@ -127,6 +131,7 @@ export(arx_forecaster)
 export(bake)
 export(cdc_baseline_args_list)
 export(cdc_baseline_forecaster)
+export(check_enough_train_data)
 export(create_layer)
 export(default_epi_recipe_blueprint)
 export(detect_layer)
@@ -191,6 +196,12 @@ import(epiprocess)
 import(parsnip)
 import(recipes)
 importFrom(cli,cli_abort)
+importFrom(dplyr,across)
+importFrom(dplyr,all_of)
+importFrom(dplyr,group_by)
+importFrom(dplyr,n)
+importFrom(dplyr,summarise)
+importFrom(dplyr,ungroup)
 importFrom(epiprocess,growth_rate)
 importFrom(generics,augment)
 importFrom(generics,fit)
@@ -225,6 +236,7 @@ importFrom(stats,residuals)
 importFrom(tibble,as_tibble)
 importFrom(tibble,is_tibble)
 importFrom(tibble,tibble)
+importFrom(tidyr,drop_na)
 importFrom(vctrs,as_list_of)
 importFrom(vctrs,field)
 importFrom(vctrs,new_rcrd)

NEWS.md

@@ -1,44 +1,49 @@
 # epipredict (development)
 
+# epipredict 0.0.8
+
+-   add `check_enough_train_data` that will error if training data is too small
+-   added `check_enough_train_data` to `arx_forecaster`
+
 # epipredict 0.0.7
 
-* simplify `layer_residual_quantiles()` to avoid timesuck in `utils::methods()`
+-   simplify `layer_residual_quantiles()` to avoid timesuck in `utils::methods()`
 
 # epipredict 0.0.6
 
-* rename the `dist_quantiles()` to be more descriptive, breaking change)
-* removes previous `pivot_quantiles()` (now `*_wider()`, breaking change)
-* add `pivot_quantiles_wider()` for easier plotting
-* add complement `pivot_quantiles_longer()`
-* add `cdc_baseline_forecaster()` and `flusight_hub_formatter()`
+-   rename the `dist_quantiles()` to be more descriptive, breaking change)
+-   removes previous `pivot_quantiles()` (now `*_wider()`, breaking change)
+-   add `pivot_quantiles_wider()` for easier plotting
+-   add complement `pivot_quantiles_longer()`
+-   add `cdc_baseline_forecaster()` and `flusight_hub_formatter()`
 
 # epipredict 0.0.5
 
-* add `smooth_quantile_reg()`
-* improved printing of various methods / internals
-* canned forecasters get a class
-* fixed quantile bug in `flatline_forecaster()`
-* add functionality to output the unfit workflow from the canned forecasters
+-   add `smooth_quantile_reg()`
+-   improved printing of various methods / internals
+-   canned forecasters get a class
+-   fixed quantile bug in `flatline_forecaster()`
+-   add functionality to output the unfit workflow from the canned forecasters
 
 # epipredict 0.0.4
 
-* add quantile_reg()
-* clean up documentation bugs
-* add smooth_quantile_reg()
-* add classifier
-* training window step debugged
-* `min_train_window` argument removed from canned forecasters
+-   add quantile_reg()
+-   clean up documentation bugs
+-   add smooth_quantile_reg()
+-   add classifier
+-   training window step debugged
+-   `min_train_window` argument removed from canned forecasters
 
 # epipredict 0.0.3
 
-* add forecasters
-* implement postprocessing
-* vignettes avaliable
-* arx_forecaster
-* pkgdown
+-   add forecasters
+-   implement postprocessing
+-   vignettes avaliable
+-   arx_forecaster
+-   pkgdown
 
 # epipredict 0.0.0.9000
 
-* Publish public for easy navigation
-* Two simple forecasters as test beds
-* Working vignette
+-   Publish public for easy navigation
+-   Two simple forecasters as test beds
+-   Working vignette

R/arx_classifier.R

@@ -180,7 +180,21 @@ arx_class_epi_workflow <- function(
       role = "outcome"
     ) %>%
     step_epi_naomit() %>%
-    step_training_window(n_recent = args_list$n_training)
+    step_training_window(n_recent = args_list$n_training) %>%
+    {
+      if (!is.null(args_list$check_enough_data_n)) {
+        check_enough_train_data(
+          .,
+          all_predictors(),
+          !!outcome,
+          n = args_list$check_enough_data_n,
+          epi_keys = args_list$check_enough_data_epi_keys,
+          drop_na = FALSE
+        )
+      } else {
+        .
+      }
+    }
 
   forecast_date <- args_list$forecast_date %||% max(epi_data$time_value)
   target_date <- args_list$target_date %||% forecast_date + args_list$ahead
@@ -228,6 +242,11 @@ arx_class_epi_workflow <- function(
 #' @param additional_gr_args List. Optional arguments controlling growth rate
 #'   calculation. See [epiprocess::growth_rate()] and the related Vignette for
 #'   more details.
+#' @param check_enough_data_n Integer. A lower limit for the number of rows per
+#'   epi_key that are required for training. If `NULL`, this check is ignored.
+#' @param check_enough_data_epi_keys Character vector. A character vector of
+#'   column names on which to group the data and check threshold within each
+#'   group. Useful if training per group (for example, per geo_value).
 #'
 #' @return A list containing updated parameter choices with class `arx_clist`.
 #' @export
@@ -251,6 +270,8 @@ arx_class_args_list <- function(
     log_scale = FALSE,
     additional_gr_args = list(),
     nafill_buffer = Inf,
+    check_enough_data_n = NULL,
+    check_enough_data_epi_keys = NULL,
     ...) {
   rlang::check_dots_empty()
   .lags <- lags
@@ -275,6 +296,8 @@ arx_class_args_list <- function(
       )
     )
   }
+  arg_is_pos(check_enough_data_n, allow_null = TRUE)
+  arg_is_chr(check_enough_data_epi_keys, allow_null = TRUE)
 
   breaks <- sort(breaks)
   if (min(breaks) > -Inf) breaks <- c(-Inf, breaks)
@@ -296,7 +319,9 @@ arx_class_args_list <- function(
       method,
       log_scale,
       additional_gr_args,
-      nafill_buffer
+      nafill_buffer,
+      check_enough_data_n,
+      check_enough_data_epi_keys
     ),
     class = c("arx_class", "alist")
   )

R/arx_forecaster.R

@@ -126,7 +126,21 @@ arx_fcast_epi_workflow <- function(
   r <- r %>%
     step_epi_ahead(!!outcome, ahead = args_list$ahead) %>%
     step_epi_naomit() %>%
-    step_training_window(n_recent = args_list$n_training)
+    step_training_window(n_recent = args_list$n_training) %>%
+    {
+      if (!is.null(args_list$check_enough_data_n)) {
+        check_enough_train_data(
+          .,
+          all_predictors(),
+          !!outcome,
+          n = args_list$check_enough_data_n,
+          epi_keys = args_list$check_enough_data_epi_keys,
+          drop_na = FALSE
+        )
+      } else {
+        .
+      }
+    }
 
   forecast_date <- args_list$forecast_date %||% max(epi_data$time_value)
   target_date <- args_list$target_date %||% forecast_date + args_list$ahead
@@ -199,6 +213,11 @@ arx_fcast_epi_workflow <- function(
 #'   create a prediction. For this reason, setting `nafill_buffer < min(lags)`
 #'   will be treated as _additional_ allowed recent data rather than the
 #'   total amount of recent data to examine.
+#' @param check_enough_data_n Integer. A lower limit for the number of rows per
+#'   epi_key that are required for training. If `NULL`, this check is ignored.
+#' @param check_enough_data_epi_keys Character vector. A character vector of
+#'   column names on which to group the data and check threshold within each
+#'   group. Useful if training per group (for example, per geo_value).
 #' @param ... Space to handle future expansions (unused).
 #'
 #'
@@ -220,6 +239,8 @@ arx_args_list <- function(
     nonneg = TRUE,
     quantile_by_key = character(0L),
     nafill_buffer = Inf,
+    check_enough_data_n = NULL,
+    check_enough_data_epi_keys = NULL,
     ...) {
   # error checking if lags is a list
   rlang::check_dots_empty()
@@ -236,6 +257,8 @@ arx_args_list <- function(
   arg_is_pos(n_training)
   if (is.finite(n_training)) arg_is_pos_int(n_training)
   if (is.finite(nafill_buffer)) arg_is_pos_int(nafill_buffer, allow_null = TRUE)
+  arg_is_pos(check_enough_data_n, allow_null = TRUE)
+  arg_is_chr(check_enough_data_epi_keys, allow_null = TRUE)
 
   max_lags <- max(lags)
   structure(
@@ -250,7 +273,9 @@ arx_args_list <- function(
       nonneg,
       max_lags,
       quantile_by_key,
-      nafill_buffer
+      nafill_buffer,
+      check_enough_data_n,
+      check_enough_data_epi_keys
     ),
     class = c("arx_fcast", "alist")
   )