Add model_runs directory (3rd attempt)

hewr/R/directory_utils.R

@@ -79,8 +79,11 @@ parse_model_batch_dir_path <- function(model_batch_dir_path) {
 #'
 #' @export
 parse_model_run_dir_path <- function(model_run_dir_path) {
-  batch_dir <- fs::path_dir(model_run_dir_path) |>
+  batch_dir <- model_run_dir_path |>
+    fs::path_dir() |>
+    fs::path_dir() |>
     fs::path_file()
+
   location <- fs::path_file(model_run_dir_path)
 
   return(c(

hewr/tests/testthat/test_directory_utils.R

@@ -26,7 +26,7 @@ invalid_model_batch_dirs <- c(
 
 to_valid_run_dir <- function(valid_batch_dir_entry, location) {
   x <- valid_batch_dir_entry
-  x$dirpath <- fs::path(x$dirname, location)
+  x$dirpath <- fs::path(x$dirname, "model_runs", location)
   x$expected <- c(
     location = location,
     x$expected

pipelines/batch/setup_test_prod_job.py

@@ -18,7 +18,7 @@
         description="Test production pipeline on small subset of locations"
     )
     parser.add_argument(
-        "tag",
+        "--tag",
         type=str,
         help="The tag name to use for the container image version",
         default=Path(Repository(os.getcwd()).head.name).stem,

pipelines/collate_plots.py

@@ -39,6 +39,7 @@ def merge_pdfs_and_save(
 def merge_pdfs_from_subdirs(
     base_dir: str | Path,
     file_name: str,
+    save_dir: str | Path = None,
     output_file_name: str = None,
     subdirs_only: list[str] = None,
     subdir_pattern="*",
@@ -59,6 +60,11 @@ def merge_pdfs_from_subdirs(
        Name of the files to merge. Must be an
        exact match.
 
+    save_dir
+       Directory in which to save the merged PDF.
+       If ``None``, use a "figures" directory in the parent directory of ``base_dir``.
+       Default ``None``.
+
     output_file_name
        Name for the merged PDF file, which will be
        saved within ``base_dir``. If ``None``,
@@ -84,6 +90,13 @@ def merge_pdfs_from_subdirs(
     -------
     None
     """
+
+    if save_dir is None:
+        save_dir = Path(base_dir).parent / "figures"
+
+    if not os.path.exists(save_dir):
+        os.makedirs(save_dir)
+
     subdirs = [
         f.name for f in Path(base_dir).glob(subdir_pattern) if f.is_dir()
     ]
@@ -101,14 +114,15 @@ def merge_pdfs_from_subdirs(
         output_file_name = file_name
 
     if len(to_merge) > 0:
-        merge_pdfs_and_save(to_merge, Path(base_dir, output_file_name))
+        merge_pdfs_and_save(to_merge, Path(save_dir, output_file_name))
 
     return None
 
 
 def process_dir(
-    dir_path: Path | str,
+    base_dir: Path | str,
     target_filenames: str | list[str],
+    save_dir: Path | str = None,
     file_prefix: str = "",
     subdirs_only: list[str] = None,
 ) -> None:
@@ -119,14 +133,17 @@ def process_dir(
 
     Parameters
     ----------
-    dir_path
-        Path to the base directory, in which the merged
-        PDFs will be saved.
+    base_dir
+        Path to the base directory in which to look
 
     target_filenames
         One or more PDFs filenames to look for in the
         subdirectories and merge.
 
+    save_dir
+        Directory in which to save the merged PDFs.
+        If ``None``, use a "figures" directory in the parent directory of ``base_dir``. Default ``None``.
+
     file_prefix
         Prefix to append to the names in `target_filenames`
         when naming the merged files.
@@ -136,17 +153,24 @@ def process_dir(
         named subdirectories. If ``None``, look in all
         subdirectories of ``base_dir``. Default ``None``.
     """
+    if save_dir is None:
+        save_dir = Path(base_dir).parent / "figures"
+
     for file_name in ensure_listlike(target_filenames):
         merge_pdfs_from_subdirs(
-            dir_path,
+            base_dir,
             file_name,
+            save_dir,
             output_file_name=file_prefix + file_name,
             subdirs_only=subdirs_only,
         )
 
 
 def collate_from_all_subdirs(
-    model_base_dir: str | Path, disease: str, target_filenames: str | list[str]
+    model_base_dir: str | Path,
+    disease: str,
+    target_filenames: str | list[str],
+    save_dir: str | Path = None,
 ) -> None:
     """
     Collate target plots for a given disease
@@ -156,8 +180,7 @@ def collate_from_all_subdirs(
     ----------
     model_base_dir
         Path to the base directory in whose subdirectories
-        the script will look for PDFs to merge and in which
-        the merged PDFs will be saved.
+        the script will look for PDFs to merge.
 
     disease
         Name of the target disease. Merged PDFs will be named
@@ -167,10 +190,17 @@ def collate_from_all_subdirs(
         One or more PDFs filenames to look for in the
         subdirectories and merge.
 
+    save_dir
+        Directory in which to save the merged PDFs.
+        If ``None``, use a "figures" directory in the parent directory of ``model_base_dir``. Default ``None``.
+
     Returns
     -------
     None
     """
+    if save_dir is None:
+        save_dir = Path(model_base_dir).parent / "figures"
+
     logging.basicConfig(level=logging.INFO)
     logger = logging.getLogger(__name__)
 
@@ -186,8 +216,9 @@ def collate_from_all_subdirs(
     for f_dir in forecast_dirs:
         logger.info(f"Collating plots from {f_dir}")
         process_dir(
-            dir_path=Path(model_base_dir, f_dir),
+            base_dir=Path(model_base_dir, f_dir),
             target_filenames=target_filenames,
+            save_dir=save_dir,
         )
     logger.info("Done collating across locations by date.")
 
@@ -197,11 +228,13 @@ def collate_from_all_subdirs(
     # for multiple diseases.
     logger.info("Collating plots from forecast date directories...")
     process_dir(
-        dir_path=model_base_dir,
+        base_dir=model_base_dir,
         target_filenames=target_filenames,
+        save_dir=save_dir,
         file_prefix=f"{disease}_",
         subdirs_only=forecast_dirs,
     )
+
     logger.info("Done collating plots from forecast date directories.")
 
     return None

pipelines/collate_score_tables.R

@@ -83,7 +83,7 @@ collate_scores_for_date <- function(model_run_dir,
                                     score_file_ext = "rds",
                                     save = FALSE) {
   message(glue::glue("Processing scores from {model_run_dir}..."))
-  locations_to_process <- fs::dir_ls(model_run_dir,
+  locations_to_process <- fs::dir_ls(model_run_dir, "model_runs",
     type = "directory"
   )
   date_score_table <- purrr::map(

pipelines/forecast_state.py

@@ -194,7 +194,7 @@ def main(
 
     model_batch_dir = Path(output_data_dir, model_batch_dir_name)
 
-    model_run_dir = Path(model_batch_dir, state)
+    model_run_dir = Path(model_batch_dir, "model_runs", state)
 
     os.makedirs(model_run_dir, exist_ok=True)
 
@@ -212,7 +212,7 @@ def main(
         first_training_date=first_training_date,
         last_training_date=last_training_date,
         param_estimates=param_estimates,
-        model_batch_dir=model_batch_dir,
+        model_run_dir=model_run_dir,
         logger=logger,
     )
     logger.info("Data preparation complete.")

pipelines/postprocess_state_forecast.R

@@ -13,7 +13,8 @@ script_packages <- c(
   "tidyr",
   "readr",
   "here",
-  "forcats"
+  "forcats",
+  "hewr"
 )
 
 ## load in packages without messages
@@ -255,12 +256,6 @@ postprocess_state_forecast <- function(model_run_dir) {
 
 theme_set(theme_minimal_grid())
 
-disease_name_formatter <- c("covid-19" = "COVID-19", "influenza" = "Flu")
-disease_name_nssp_map <- c(
-  "covid-19" = "COVID-19",
-  "influenza" = "Influenza"
-)
-
 # Create a parser
 p <- arg_parser("Generate forecast figures") |>
   add_argument(
@@ -271,13 +266,10 @@ p <- arg_parser("Generate forecast figures") |>
 argv <- parse_args(p)
 model_run_dir <- path(argv$model_run_dir)
 
-base_dir <- path_dir(model_run_dir)
 
-disease_name_raw <- base_dir |>
-  path_file() |>
-  str_extract("^.+(?=_r_)")
+disease_name_nssp <- parse_model_run_dir_path(model_run_dir)$disease
 
-disease_name_nssp <- unname(disease_name_nssp_map[disease_name_raw])
-disease_name_pretty <- unname(disease_name_formatter[disease_name_raw])
+disease_name_formatter <- c("COVID-19" = "COVID-19", "Influenza" = "Flu")
+disease_name_pretty <- unname(disease_name_formatter[disease_name_nssp])
 
 postprocess_state_forecast(model_run_dir)

pipelines/prep_data.py

@@ -219,7 +219,7 @@ def process_and_save_state(
     first_training_date: datetime.date,
     last_training_date: datetime.date,
     param_estimates: pl.LazyFrame,
-    model_batch_dir: Path,
+    model_run_dir: Path,
     logger: Logger = None,
     facility_level_nssp_data: pl.LazyFrame = None,
     state_level_nssp_data: pl.LazyFrame = None,
@@ -333,13 +333,15 @@ def process_and_save_state(
         "right_truncation_offset": right_truncation_offset,
     }
 
-    state_dir = os.path.join(model_batch_dir, state_abb)
-    os.makedirs(state_dir, exist_ok=True)
+    os.makedirs(model_run_dir, exist_ok=True)
 
-    logger.info(f"Saving {state_abb} to {state_dir}")
-    data_to_save.write_csv(Path(state_dir, "data.csv"))
+    if logger is not None:
+        logger.info(f"Saving {state_abb} to {model_run_dir}")
+    data_to_save.write_csv(Path(model_run_dir, "data.csv"))
 
-    with open(Path(state_dir, "data_for_model_fit.json"), "w") as json_file:
+    with open(
+        Path(model_run_dir, "data_for_model_fit.json"), "w"
+    ) as json_file:
         json.dump(data_for_model_fit, json_file)
 
     return None

pipelines/timeseries_forecasts.R

@@ -11,7 +11,11 @@ script_packages <- c(
   "arrow",
   "glue",
   "epipredict",
-  "epiprocess"
+  "epiprocess",
+  "purrr",
+  "rlang",
+  "glue",
+  "hewr"
 )
 
 ## load in packages without messages
@@ -29,12 +33,12 @@ to_prop_forecast <- function(forecast_disease_count,
                              other_count_col =
                                "other_ed_visits",
                              output_col = "prop_disease_ed_visits") {
-  result <- dplyr::inner_join(
+  result <- inner_join(
     forecast_disease_count,
     forecast_other_count,
     by = c(".draw", "date")
   ) |>
-    dplyr::mutate(
+    mutate(
       !!output_col :=
         .data[[disease_count_col]] /
           (.data[[disease_count_col]] +
@@ -68,9 +72,9 @@ fit_and_forecast <- function(data,
                              n_samples = 2000,
                              target_col = "ed_visits",
                              output_col = "other_ed_visits") {
-  forecast_horizon <- glue::glue("{n_forecast_days} days")
-  target_sym <- rlang::sym(target_col)
-  output_sym <- rlang::sym(output_col)
+  forecast_horizon <- glue("{n_forecast_days} days")
+  target_sym <- sym(target_col)
+  output_sym <- sym(output_col)
 
   max_visits <- data |>
     pull(!!target_sym) |>
@@ -200,21 +204,21 @@ main <- function(model_run_dir, n_forecast_days = 28, n_samples = 2000) {
     aheads = 1:n_forecast_days
   )
 
-  to_save <- tibble::tribble(
+  to_save <- tribble(
     ~basename, ~value,
     "other_ed_visits_forecast", forecast_other,
     "baseline_ts_count_ed_visits_forecast", baseline_ts_count,
     "baseline_ts_prop_ed_visits_forecast", baseline_ts_prop,
     "baseline_cdc_count_ed_visits_forecast", baseline_cdc_count,
     "baseline_cdc_prop_ed_visits_forecast", baseline_cdc_prop
   ) |>
-    dplyr::mutate(save_path = path(
+    mutate(save_path = path(
       !!model_run_dir, basename,
       ext = "parquet"
     ))
 
 
-  purrr::walk2(
+  walk2(
     to_save$value,
     to_save$save_path,
     write_parquet
@@ -250,12 +254,6 @@ disease_name_nssp_map <- c(
   "influenza" = "Influenza"
 )
 
-base_dir <- path_dir(model_run_dir)
-
-disease_name_raw <- base_dir |>
-  path_file() |>
-  str_extract("^.+(?=_r_)")
-
-disease_name_nssp <- unname(disease_name_nssp_map[disease_name_raw])
+disease_name_nssp <- parse_model_run_dir_path(model_run_dir)$disease
 
 main(model_run_dir, n_forecast_days, n_samples)