testing forecasters on simple datasets

.Rbuildignore

@@ -1,2 +1,6 @@
 ^renv$
 ^renv\.lock$
+^LICENSE\.md$
+^.lintr$
+^.renvignore$
+^.github$

NAMESPACE

@@ -5,12 +5,16 @@ export(add_id)
 export(arx_postprocess)
 export(arx_preprocess)
 export(collapse_cards)
+export(confirm_insufficient_data)
 export(covidhub_probs)
 export(evaluate_predictions)
 export(extend_ahead)
+export(flatline_fc)
 export(forecaster_pred)
 export(format_storage)
+export(id_ahead_ensemble_grid)
 export(interval_coverage)
+export(lookup_ids)
 export(make_target_param_grid)
 export(overprediction)
 export(perform_sanity_checks)
@@ -19,6 +23,7 @@ export(run_evaluation_measure)
 export(run_workflow_and_format)
 export(scaled_pop)
 export(sharpness)
+export(single_id)
 export(underprediction)
 export(weighted_interval_score)
 import(dplyr)
@@ -38,6 +43,7 @@ importFrom(purrr,map)
 importFrom(purrr,transpose)
 importFrom(rlang,.data)
 importFrom(rlang,quo)
+importFrom(rlang,sym)
 importFrom(rlang,syms)
 importFrom(tibble,tibble)
 importFrom(tidyr,pivot_wider)

R/forecaster.R

@@ -28,18 +28,44 @@ perform_sanity_checks <- function(epi_data,
   if (!is.null(trainer) && !epipredict:::is_regression(trainer)) {
     cli::cli_abort("{trainer} must be a `{parsnip}` model of mode 'regression'.")
   } else if (inherits(trainer, "quantile_reg")) {
-    # add all levels to the trainer and update args list
-    tau <- sort(epipredict:::compare_quantile_args(
-      args_list$levels,
-      rlang::eval_tidy(trainer$args$tau)
+    # add all quantile_levels to the trainer and update args list
+    quantile_levels <- sort(epipredict:::compare_quantile_args(
+      args_list$quantile_levels,
+      rlang::eval_tidy(trainer$args$quantile_levels)
     ))
-    args_list$levels <- tau
-    trainer$args$tau <- rlang::enquo(tau)
+    args_list$quantile_levels <- quantile_levels
+    trainer$args$quantile_levels <- rlang::enquo(quantile_levels)
   }
   args_list$lags <- epipredict:::arx_lags_validator(predictors, args_list$lags)
   return(list(args_list, predictors, trainer))
 }
 
+#' confirm that there's enough data to run this model
+#' @description
+#' epipredict is a little bit fragile about having enough data to train; we want
+#'   to be able to return a null result rather than error out; this check say to
+#'   return a null
+#' @param epi_data the input data
+#' @param buffer how many training data to insist on having (e.g. if `buffer=1`,
+#'   this trains on one sample; the default is set so that `linear_reg` isn't
+#'   rank deficient)
+#' @param ahead the effective ahead; may be infinite if there isn't enough data.
+#' @param args_input the input as supplied to `forecaster_pred`; lags is the
+#'   important argument, which may or may not be defined, with the default
+#'   coming from `arx_args_list`
+#' @export
+confirm_insufficient_data <- function(epi_data, ahead, args_input, buffer = 9) {
+  if (!is.null(args_input$lags)) {
+    lag_max <- max(args_input$lags)
+  } else {
+    lag_max <- 14 # default value of 2 weeks
+  }
+  return(
+    is.infinite(ahead) ||
+      as.integer(max(epi_data$time_value) - min(epi_data$time_value)) <=
+        lag_max + ahead + buffer
+  )
+}
 # TODO replace with `step_arx_forecaster`
 #' add the default steps for arx_forecaster
 #' @description
@@ -86,20 +112,18 @@ arx_postprocess <- function(postproc,
                             target_date = NULL) {
   postproc %<>% layer_predict()
   if (inherits(trainer, "quantile_reg")) {
-
-    postproc %<>% layer_quantile_distn(levels = args_list$levels) %>% layer_point_from_distn()
+    postproc %<>% layer_quantile_distn(quantile_levels = args_list$quantile_levels) %>% layer_point_from_distn()
   } else {
     postproc %<>% layer_residual_quantiles(
-      probs = args_list$levels, symmetrize = args_list$symmetrize,
+      quantile_levels = args_list$quantile_levels, symmetrize = args_list$symmetrize,
       by_key = args_list$quantile_by_key
     )
   }
   if (args_list$nonneg) {
     postproc %<>% layer_threshold(dplyr::starts_with(".pred"))
   }
 
-  postproc %<>% layer_naomit(dplyr::starts_with(".pred"))
-  postproc %<>% layer_add_forecast_date(forecast_date = forecast_date) %>%
+  postproc %<>% layer_naomit(dplyr::starts_with(".pred")) %>%
     layer_add_target_date(target_date = target_date)
   return(postproc)
 }
@@ -162,6 +186,14 @@ forecaster_pred <- function(data,
   if (length(forecaster_args) > 0) {
     names(forecaster_args) <- forecaster_args_names
   }
+  if (is.null(forecaster_args$ahead)) {
+    cli::cli_abort(
+      c(
+        "exploration-tooling error: forecaster_pred needs some value for ahead."
+      ),
+      class = "explorationToolingError"
+    )
+  }
   if (!is.numeric(forecaster_args$n_training) && !is.null(forecaster_args$n_training)) {
     n_training <- as.numeric(forecaster_args$n_training)
     net_slide_training <- max(slide_training, n_training) + n_training_pad
@@ -171,11 +203,6 @@ forecaster_pred <- function(data,
   }
   # restrict the dataset to areas where training is possible
   start_date <- min(archive$DT$time_value) + net_slide_training
-  if (slide_training < Inf) {
-    start_date <- min(archive$DT$time_value) + slide_training + n_training_pad
-  } else {
-    start_date <- min(archive$DT$time_value) + n_training_pad
-  }
   end_date <- max(archive$DT$time_value) - forecaster_args$ahead
   valid_predict_dates <- seq.Date(from = start_date, to = end_date, by = 1)
 
@@ -206,7 +233,7 @@ forecaster_pred <- function(data,
 
   # append the truth data
   true_value <- archive$as_of(archive$versions_end) %>%
-    select(geo_value, time_value, outcome) %>%
+    select(geo_value, time_value, !!outcome) %>%
     rename(true_value = !!outcome)
   res %<>%
     inner_join(true_value,

R/forecaster_flatline.R

@@ -0,0 +1,54 @@
+#' flatline forecaster (aka baseline)
+#' @description
+#' a minimal forecaster whose median is just the last value
+#' does not support `lags` as a parameter, but otherwise has the same parameters as `arx_forecaster`
+#' @inheritParams scaled_pop
+#' @importFrom rlang sym
+#' @export
+flatline_fc <- function(epi_data,
+                        outcome,
+                        extra_sources = "",
+                        ahead = 1,
+                        trainer = parsnip::linear_reg(),
+                        quantile_levels = covidhub_probs(),
+                        ...) {
+  # perform any preprocessing not supported by epipredict
+  # one that every forecaster will need to handle: how to manage max(time_value)
+  # that's older than the `as_of` date
+  epidataAhead <- extend_ahead(epi_data, ahead)
+  # see latency_adjusting for other examples
+  # this next part is basically unavoidable boilerplate you'll want to copy
+  epi_data <- epidataAhead[[1]]
+  effective_ahead <- epidataAhead[[2]]
+  args_input <- list(...)
+  # edge case where there is no data or less data than the lags; eventually epipredict will handle this
+  if (confirm_insufficient_data(epi_data, effective_ahead, args_input)) {
+    null_result <- tibble(
+      geo_value = character(),
+      forecast_date = Date(),
+      target_end_date = Date(),
+      quantile = numeric(),
+      value = numeric()
+    )
+    return(null_result)
+  }
+  args_input[["ahead"]] <- effective_ahead
+  args_input[["quantile_levels"]] <- quantile_levels
+  args_list <- do.call(flatline_args_list, args_input)
+  # if you want to ignore extra_sources, setting predictors is the way to do it
+  predictors <- c(outcome, extra_sources)
+  argsPredictorsTrainer <- perform_sanity_checks(epi_data, outcome, predictors, NULL, args_list)
+  args_list <- argsPredictorsTrainer[[1]]
+  predictors <- argsPredictorsTrainer[[2]]
+  # end of the copypasta
+  # finally, any other pre-processing (e.g. smoothing) that isn't performed by
+  # epipredict
+
+  # since this is just the flatline, we don't need much of anything
+  res <- flatline_forecaster(epi_data, outcome = outcome, args_list = args_list)
+  true_forecast_date <- attributes(epi_data)$metadata$as_of
+  pred <- format_storage(res$predictions, true_forecast_date)
+  # (geo_value, forecast_date, target_end_date, quantile, value)
+  # finally, any postprocessing not supported by epipredict e.g. calibration
+  return(pred)
+}

R/forecaster_scaled_pop.R

@@ -32,7 +32,7 @@
 #'   should be ones that will store well in a data.table; if you need more
 #'   complicated parameters, it is better to store them in separate files, and
 #'   use the filename as the parameter.
-#' @param levels The quantile levels to predict. Defaults to those required by
+#' @param quantile_levels The quantile levels to predict. Defaults to those required by
 #'   covidhub.
 #' @seealso some utilities for making forecasters: [format_storage],
 #'   [perform_sanity_checks]
@@ -45,10 +45,10 @@
 scaled_pop <- function(epi_data,
                        outcome,
                        extra_sources = "",
-                       ahead=1,
+                       ahead = 1,
                        pop_scaling = TRUE,
                        trainer = parsnip::linear_reg(),
-                       levels = covidhub_probs(),
+                       quantile_levels = covidhub_probs(),
                        ...) {
   # perform any preprocessing not supported by epipredict
   # one that every forecaster will need to handle: how to manage max(time_value)
@@ -58,9 +58,9 @@ scaled_pop <- function(epi_data,
   # this next part is basically unavoidable boilerplate you'll want to copy
   epi_data <- epidataAhead[[1]]
   effective_ahead <- epidataAhead[[2]]
-  # edge case where there is no data; eventually epipredict will handle this
-  if (is.infinite(effective_ahead)) {
-    effective_ahead <- 0
+  args_input <- list(...)
+  # edge case where there is no data or less data than the lags; eventually epipredict will handle this
+  if (confirm_insufficient_data(epi_data, effective_ahead, args_input)) {
     null_result <- tibble(
       geo_value = character(),
       forecast_date = Date(),
@@ -70,9 +70,8 @@ scaled_pop <- function(epi_data,
     )
     return(null_result)
   }
-  args_input <- list(...)
   args_input[["ahead"]] <- effective_ahead
-  args_input[["levels"]] <- levels
+  args_input[["quantile_levels"]] <- quantile_levels
   args_list <- do.call(arx_args_list, args_input)
   # if you want to ignore extra_sources, setting predictors is the way to do it
   predictors <- c(outcome, extra_sources)

R/formatters.R

@@ -18,8 +18,8 @@ format_storage <- function(pred, true_forecast_date, target_end_date) {
       .dstn = nested_quantiles(.pred_distn)
     ) %>%
     unnest(.dstn) %>%
-    select(-.pred_distn, -.pred, -time_value) %>%
-    rename(quantile = tau, value = q, target_end_date = target_date) %>%
+    select(-any_of(c(".pred_distn", ".pred", "time_value"))) %>%
+    rename(quantile = quantile_levels, value = values, target_end_date = target_date) %>%
     relocate(geo_value, forecast_date, target_end_date, quantile, value)
 }
 
@@ -33,13 +33,13 @@ format_storage <- function(pred, true_forecast_date, target_end_date) {
 #' @param true_forecast_date the actual date from which the model is
 #'   making the forecast, rather than the last day of available data
 #' @param target_end_date the date of the prediction
-#' @param levels the quantile levels
+#' @param quantile_levels the quantile levels
 #' @import dplyr
-format_covidhub <- function(pred, true_forecast_date, target_end_date, levels) {
+format_covidhub <- function(pred, true_forecast_date, target_end_date, quantile_levels) {
   pred %<>%
     group_by(forecast_date, geo_value, target_date) %>%
     rename(target_end_date = target_date) %>%
-    reframe(quantile = levels, value = quantile(.pred_distn, levels)[[1]])
+    reframe(quantile = quantile_levels, value = quantile(.pred_distn, quantile_levels)[[1]])
   forecasts$ahead <- ahead
   forecasts %<>%
     group_by(forecast_date, geo_value, target_date) %>%

R/small_utils.R

@@ -20,16 +20,63 @@ covidhub_probs <- function(type = c("standard", "inc_case")) {
 #' @importFrom cli hash_animal
 #' @export
 add_id <- function(df, n_adj = 2) {
-  stringified <- df %>%
-    select(-ahead) %>%
+  no_ahead <- df %>%
+    select(-ahead)
+  stringified <- no_ahead %>%
+    select(order(colnames(no_ahead))) %>%
     rowwise() %>%
-    mutate(id = paste(across(everything()), collapse = ""), .keep="none") %>%
+    mutate(id = paste(across(everything()), sep = "", collapse = ""), .keep = "none") %>%
     mutate(id = hash_animal(id, n_adj = n_adj)$words) %>%
-    mutate(id = paste(id[1:n_adj], sep="", collapse = " "))
+    mutate(id = paste(id[1:n_adj], sep = "", collapse = "."))
   df %<>%
+    ungroup %>%
     mutate(parent_id = stringified$id) %>%
     rowwise() %>%
-    mutate(id = paste(parent_id, ahead, collapse = " ")) %>%
+    mutate(id = paste(parent_id, ahead, sep = ".", collapse = " ")) %>%
     ungroup()
   return(df)
 }
+
+#' generate an id from a simple list of parameters
+#' @param param_list the list of parameters. must include `ahead` if `ahead = NULL`
+#' @param ahead the ahead to use.
+#' @inheritParams add_id
+#' @export
+single_id <- function(param_list, ahead = NULL, n_adj = 2) {
+  full_hash <- param_list[names(param_list) != "ahead"] %>%
+    .[order(names(.))] %>% # put in alphabetical order
+    paste(collapse = "") %>%
+    hash_animal(n_adj = n_adj)
+  single_string <- full_hash$words[[1]][1:n_adj] %>% paste(sep = ".", collapse = ".")
+  if (is.null(ahead)) {
+    full_name <- paste(single_string, param_list$ahead, sep = ".")
+  } else {
+    full_name <- paste(single_string, ahead, sep = ".")
+  }
+  return(full_name)
+}
+#' given target name(s), lookup the corresponding parameters
+#' @export
+lookup_ids <- function() {
+}
+
+
+#' add aheads, forecaster_ids, and ids to a list of ensemble models
+#' @description
+#' minor utility
+#' @param ensemble_grid the list of ensembles,
+#' @param aheads the aheads to add
+#' @inheritParams add_id
+#' @export
+id_ahead_ensemble_grid <- function(ensemble_grid, aheads, n_adj = 2) {
+  ensemble_grid <- expand_grid(
+    ensemble_grid,
+    tibble(ahead = aheads)
+  )
+
+  ensemble_grid %<>%
+    add_id(., n_adj = 2) %>%
+    rowwise() %>%
+    mutate(forecaster_ids = list(map2_vec(forecasters, ahead, single_id, n_adj = 2)))
+  return(ensemble_grid)
+}

_targets.yaml

@@ -14,7 +14,3 @@ covid_hosp_prod:
   script: covid_hosp_prod.R
   store: covid_hosp_prod
   use_crew: yes
-forecaster_testing:
-  script: forecaster_testing.R
-  store: forecaster_testing
-  use_crew: no

covid_hosp_explore.R

@@ -28,7 +28,7 @@ tar_option_set(
   imports = c("epieval", "parsnip"),
   format = "qs", # Optionally set the default storage format. qs is fast.
   controller = crew::crew_controller_local(workers = parallel::detectCores() - 5),
-  )
+)
 # Run the R scripts in the R/ folder with your custom functions:
 # tar_source()
 # where the forecasters and parameters are joined; see either the variable param_grid or `tar_read(forecasters)`