Climatological backfill

Makefile

@@ -86,7 +86,7 @@ dashboard:
 	Rscript scripts/dashboard.R
 
 update_site:
-	Rscript -e "source('R/utils.R'); update_site()"
+	Rscript -e "suppressPackageStartupMessages(source(here::here('R', 'load_all.R'))); update_site()"
 
 netlify:
 	netlify deploy --dir=reports --prod

R/aux_data_utils.R

@@ -617,3 +617,94 @@ process_nhsn_data <- function(raw_nhsn_data) {
     mutate(version = fixed_version) %>%
     relocate(geo_value, disease, time_value, version)
 }
+
+
+# for filenames of the form nhsn_data_2024-11-19_16-29-43.191649.rds
+get_version_timestamp <- function(filename) ymd_hms(str_match(filename, "[0-9]{4}-..-.._..-..-..\\.[^.^_]*"))
+
+#' all in one function to get and cache a nhsn archive from raw files
+#' @description
+#' This takes in all of the raw data files for the nhsn data, creates a
+#'   quasi-archive (it keeps one example per version-day, rather than one per
+#'   change), and puts it in the bucket. The stored value has the columns
+#'   geo_value, time_value, disease, endpoint (either basic or prelim), version,
+#'   version_timestamp (to enable keeping the most recent value), and value.
+#'   The returned value on the other hand is an actual epi_archive, only
+#'   containing the data for `disease_name`.
+create_nhsn_data_archive <- function(disease_name) {
+  if (aws.s3::head_object("archive_timestamped.parquet", bucket = "forecasting-team-data")) {
+    aws.s3::save_object("archive_timestamped.parquet", bucket = "forecasting-team-data", file = here::here("cache/archive_timestamped.parquet"))
+    previous_archive <- qs::qread(here::here("cache/archive_timestamped.parquet"))
+    last_timestamp <- max(previous_archive$version_timestamp)
+  } else {
+    # there is no remote
+    previous_archive <- NULL
+    last_timestamp <- as.Date("1000-01-01")
+  }
+  new_data <- aws.s3::get_bucket_df(bucket = "forecasting-team-data", prefix = "nhsn_data_") %>%
+    filter(get_version_timestamp(Key) > last_timestamp) %>%
+    pull(Key) %>%
+    lapply(
+      function(filename) {
+        version_timestamp <- get_version_timestamp(filename)
+        res <- NULL
+        tryCatch(
+          {
+            s3load(object = filename, bucket = "forecasting-team-data")
+            if (grepl("prelim", filename)) {
+              res <- epi_data_raw_prelim
+              endpoint_val <- "prelim"
+            } else {
+              res <- epi_data_raw
+              endpoint_val <- "basic"
+            }
+            res <- res %>%
+              process_nhsn_data() %>%
+              select(geo_value, disease, time_value, value) %>%
+              mutate(version_timestamp = version_timestamp, endpoint = endpoint_val)
+          },
+          error = function(cond) {}
+        )
+        res
+      }
+    )
+  # drop any duplicates on the same day
+  compactified <-
+    new_data %>%
+    bind_rows()
+  if (nrow(compactified) == 0) {
+    one_per_day <- previous_archive
+  } else {
+    compactified <-
+      compactified %>%
+      arrange(geo_value, time_value, disease, endpoint, version_timestamp) %>%
+      mutate(version = as.Date(version_timestamp)) %>%
+      filter(if_any(
+        c(everything(), -endpoint, -version_timestamp), # all non-version, non-endpoint columns
+        ~ !epiprocess:::is_locf(., .Machine$double.eps^0.5)
+      ))
+
+    unchanged <- previous_archive %>% filter(!(version %in% unique(compactified$version)))
+    # only keep the last value for a given version (so across version_timestamps)
+    # we only need to do this for the versions in compactified, as the other versions can't possibly change
+    one_per_day <-
+      previous_archive %>%
+      filter(version %in% unique(compactified$version)) %>%
+      bind_rows(compactified) %>%
+      group_by(geo_value, disease, time_value, version) %>%
+      arrange(version_timestamp) %>%
+      filter(row_number() == n()) %>%
+      ungroup() %>%
+      bind_rows(unchanged)
+    qs::qsave(one_per_day, here::here("cache/archive_timestamped.parquet"))
+    aws.s3::put_object(here::here("cache/archive_timestamped.parquet"), "archive_timestamped.parquet", bucket = "forecasting-team-data")
+  }
+  one_per_day %>%
+    filter(disease == disease_name) %>%
+    select(-version_timestamp, -endpoint, -disease) %>%
+    mutate(
+      geo_value = ifelse(geo_value == "usa", "us", geo_value)
+    ) %>%
+    filter(geo_value != "mp") %>%
+    as_epi_archive(compactify = TRUE)
+}

R/forecasters/forecaster_climatological.R

@@ -1,18 +1,18 @@
 climate_linear_ensembled <- function(epi_data,
-                           outcome,
-                           extra_sources = "",
-                           ahead = 7,
-                           trainer = parsnip::linear_reg(),
-                           quantile_levels = covidhub_probs(),
-                           filter_source = "",
-                           filter_agg_level = "",
-                           scale_method = c("quantile", "std", "none"),
-                           center_method = c("median", "mean", "none"),
-                           nonlin_method = c("quart_root", "none"),
-                           drop_non_seasons = FALSE,
-                           residual_tail = 0.99,
-                           residual_center = 0.35,
-                           ...) {
+                                     outcome,
+                                     extra_sources = "",
+                                     ahead = 7,
+                                     trainer = parsnip::linear_reg(),
+                                     quantile_levels = covidhub_probs(),
+                                     filter_source = "",
+                                     filter_agg_level = "",
+                                     scale_method = c("quantile", "std", "none"),
+                                     center_method = c("median", "mean", "none"),
+                                     nonlin_method = c("quart_root", "none"),
+                                     drop_non_seasons = FALSE,
+                                     residual_tail = 0.99,
+                                     residual_center = 0.35,
+                                     ...) {
   scale_method <- arg_match(scale_method)
   center_method <- arg_match(center_method)
   nonlin_method <- arg_match(nonlin_method)
@@ -49,7 +49,9 @@ climate_linear_ensembled <- function(epi_data,
   }
   learned_params <- calculate_whitening_params(season_data, outcome, scale_method, center_method, nonlin_method)
   epi_data %<>% data_whitening(outcome, learned_params, nonlin_method)
-  epi_data <- epi_data %>% select(geo_value, source, time_value, season, value = !!outcome) %>% mutate(epiweek = epiweek(time_value))
+  epi_data <- epi_data %>%
+    select(geo_value, source, time_value, season, value = !!outcome) %>%
+    mutate(epiweek = epiweek(time_value))
   pred_climate <- climatological_model(epi_data, ahead) %>% mutate(forecaster = "climate")
   pred_geo_climate <- climatological_model(epi_data, ahead, geo_agg = FALSE) %>% mutate(forecaster = "climate_geo")
   pred_linear <- forecaster_baseline_linear(epi_data, ahead, residual_tail = residual_tail, residual_center = residual_center) %>% mutate(forecaster = "linear")

R/forecasters/forecaster_flusion.R

@@ -40,7 +40,7 @@ flusion <- function(epi_data,
   adding_source <- FALSE
   if (!("source" %in% names(epi_data))) {
     adding_source <- TRUE
-    epi_data$source <- c("none")
+    epi_data$source <- c("nhsn")
     attributes(epi_data)$metadata$other_keys <- "source"
   }
   if ("season_week" %nin% names(epi_data) | "season" %nin% names(epi_data)) {
@@ -146,7 +146,7 @@ flusion <- function(epi_data,
   preproc %<>%
     add_role(all_of(starts_with("slide_value")), new_role = "pre-predictor")
   # one-hot encoding of the data source
-  if (all(levels(epi_data$source) != "none") && dummy_source) {
+  if (all(levels(epi_data$source) != "nhsn") && dummy_source) {
     preproc %<>% step_dummy(source, one_hot = TRUE, keep_original_cols = TRUE, role = "pre-predictor")
   }
   # one-hot encoding of location

R/forecasters/forecaster_no_recent_outcome.R

@@ -42,7 +42,7 @@ no_recent_outcome <- function(epi_data,
   adding_source <- FALSE
   if (!("source" %in% names(epi_data))) {
     adding_source <- TRUE
-    epi_data$source <- c("none")
+    epi_data$source <- c("nhsn")
     attributes(epi_data)$metadata$other_keys <- "source"
   }
   if ("season_week" %nin% names(epi_data)) {
@@ -59,10 +59,13 @@ no_recent_outcome <- function(epi_data,
   args_input[["quantile_levels"]] <- quantile_levels
   args_list <- do.call(default_args_list, args_input)
   # if you want to hardcode particular predictors in a particular forecaster
-  predictors <- extra_sources[[1]]
-  # TODO: Partial match quantile_level coming from here (on Dmitry's machine)
+  predictors <- c(outcome, extra_sources[[1]])
   c(args_list, tmp_pred, trainer) %<-% sanitize_args_predictors_trainer(epi_data, outcome, predictors, trainer, args_list)
-
+  if (extra_sources[[1]] == "") {
+    predictors <- character()
+  } else {
+    predictors <- extra_sources[[1]]
+  }
   # end of the copypasta
   # finally, any other pre-processing (e.g. smoothing) that isn't performed by
   # epipredict

R/forecasters/forecaster_scaled_pop.R

@@ -54,7 +54,7 @@ scaled_pop <- function(epi_data,
                        scale_method = c("none", "quantile", "std"),
                        center_method = c("median", "mean", "none"),
                        nonlin_method = c("quart_root", "none"),
-                       trainer = parsnip::linear_reg(),
+                       trainer = epipredict::quantile_reg(),
                        quantile_levels = covidhub_probs(),
                        filter_source = "",
                        filter_agg_level = "",

R/forecasters/forecaster_scaled_pop_seasonal.R

@@ -48,7 +48,7 @@ scaled_pop_seasonal <- function(epi_data,
                                 seasonal_backward_window = 5 * 7,
                                 seasonal_forward_window = 3 * 7,
                                 train_residual = FALSE,
-                                trainer = parsnip::linear_reg(),
+                                trainer = epipredict::quantile_reg(),
                                 quantile_levels = covidhub_probs(),
                                 filter_source = "",
                                 filter_agg_level = "",

R/forecasters/forecaster_smoothed_scaled.R

@@ -94,7 +94,7 @@ smoothed_scaled <- function(epi_data,
   adding_source <- FALSE
   if (!("source" %in% names(epi_data))) {
     adding_source <- TRUE
-    epi_data$source <- c("none")
+    epi_data$source <- c("nhsn")
     attributes(epi_data)$metadata$other_keys <- "source"
   }
   args_input[["ahead"]] <- ahead

R/utils.R

@@ -172,18 +172,19 @@ data_substitutions <- function(dataset, disease, forecast_generation_date) {
 }
 
 parse_prod_weights <- function(filename = here::here("covid_geo_exclusions.csv"),
-                               gen_forecast_date) {
+                               forecast_date_int, forecaster_fns) {
+  forecast_date <- as.Date(forecast_date_int)
   all_states <- c(
     unique(readr::read_csv("https://raw.githubusercontent.com/cmu-delphi/covidcast-indicators/refs/heads/main/_delphi_utils_python/delphi_utils/data/2020/state_pop.csv", show_col_types = FALSE)$state_id),
     "usa", "us"
   )
   all_prod_weights <- readr::read_csv(filename, comment = "#", show_col_types = FALSE)
   # if we haven't set specific weights, use the overall defaults
-  useful_prod_weights <- filter(all_prod_weights, forecast_date == gen_forecast_date)
+  useful_prod_weights <- filter(all_prod_weights, forecast_date == forecast_date)
   if (nrow(useful_prod_weights) == 0) {
     useful_prod_weights <- all_prod_weights %>%
       filter(forecast_date == min(forecast_date)) %>%
-      mutate(forecast_date = gen_forecast_date)
+      mutate(forecast_date = forecast_date)
   }
   useful_prod_weights %>%
     mutate(
@@ -196,7 +197,7 @@ parse_prod_weights <- function(filename = here::here("covid_geo_exclusions.csv")
     unnest_longer(forecaster) %>%
     group_by(forecast_date, forecaster, geo_value) %>%
     summarize(weight = min(weight), .groups = "drop") %>%
-    mutate(forecast_date = as.Date(forecast_date)) %>%
+    mutate(forecast_date = as.Date(forecast_date_int)) %>%
     group_by(forecast_date, geo_value) %>%
     mutate(weight = ifelse(near(weight, 0), 0, weight / sum(weight)))
 }
@@ -280,7 +281,8 @@ write_submission_file <- function(pred, forecast_reference_date, submission_dire
 #' Utility to get the reference date for a given date. This is the last day of
 #' the epiweek that the date falls in.
 get_forecast_reference_date <- function(date) {
-  MMWRweek::MMWRweek2Date(epiyear(date), epiweek(date)) + 6
+    date <- as.Date(date)
+  MMWRweek::MMWRweek2Date(lubridate::epiyear(date), lubridate::epiweek(date)) + 6
 }
 
 update_site <- function() {
@@ -303,15 +305,31 @@ update_site <- function() {
     stop("Template file does not exist.")
   }
   report_md_content <- readLines(template_path)
-
   # Get the list of files in the reports directory
-  report_files <- dir_ls(reports_dir, regexp = ".*_prod.html")
+  report_files <- dir_ls(reports_dir, regexp = ".*_prod_on_.*.html")
+  report_table <- tibble(
+    filename = report_files,
+    dates = str_match_all(filename, "[0-9]{4}-..-..")
+  ) %>%
+    unnest_wider(dates, names_sep = "_") %>%
+    rename(forecast_date = dates_1, generation_date = dates_2) %>%
+    mutate(
+      forecast_date = ymd(forecast_date),
+      generation_date = ymd(generation_date),
+      disease = str_match(filename, "flu|covid")
+    )
 
-  # Extract dates and sort files by date in descending order
-  report_files <- report_files[order(as.Date(str_extract(report_files, "\\d{4}-\\d{2}-\\d{2}")), decreasing = FALSE)]
+  # use the most recently generated forecast, and sort descending on the
+  # forecast date
+  used_reports <- report_table %>%
+    group_by(forecast_date, disease) %>%
+    arrange(generation_date) %>%
+    filter(generation_date == max(generation_date)) %>%
+    ungroup() %>%
+    arrange(forecast_date)
 
   # Process each report file
-  for (report_file in report_files) {
+  for (report_file in used_reports$filename) {
     file_name <- path_file(report_file)
     file_parts <- str_split(fs::path_ext_remove(file_name), "_", simplify = TRUE)
     date <- file_parts[1]

scripts/covid_hosp_explore.R

@@ -9,7 +9,7 @@ if (!exists("ref_time_values_")) {
   start_date <- as.Date("2023-10-04")
   end_date <- as.Date("2024-04-24")
   date_step <- 7L
-  #ref_time_values_ <- as.Date(c("2023-11-08", "2023-11-22"))
+  # ref_time_values_ <- as.Date(c("2023-11-08", "2023-11-22"))
 }
 time_value_adjust <- 3 # this moves the week marker from Saturday to Wednesday