Merge pull request #10 from CDCgov/9-remove-extraneous-code-numpydoc-…

…ignore=gl08-utf-8-encoding

Ignore Extraneous Comments In Code

model_comparison/compare.R

@@ -0,0 +1,175 @@
+library(dplyr)
+library(tidyr)
+library(readr)
+library(lubridate)
+library(forecasttools)
+
+read_posterior_samples <- function(file_path) {
+  posterior_samples <- read_csv(file_path)
+  return(posterior_samples)
+}
+
+pivot_forecast_to_long <- function(
+  posterior_samples,
+  signal_name = "hosp",
+  time_name = "date"
+) {
+  draws_wide <- tibble::tibble(as.data.frame(posterior_samples))
+  names(draws_wide) <- posterior_samples$time
+  draws_long <- draws_wide %>%
+    dplyr::mutate(.draw = dplyr::row_number()) %>%
+    tidyr::pivot_longer(cols = -.draw, names_to = time_name, values_to = signal_name)
+  return(draws_long)
+}
+aggregate_to_epiweekly <- function(tidy_daily_trajectories) {
+  epiweekly_forecasts <- forecasttools::daily_to_epiweekly(
+    tidy_daily_trajectories,
+    value_col = "hosp",
+    id_cols = c(".draw"),
+    weekly_value_name = "weekly_hosp"
+  )
+  return(epiweekly_forecasts)
+}
+
+output_flusight_table <- function(
+  weekly_forecasts,
+  reference_date,
+  horizons,
+  output_path
+) {
+  formatted_output <- forecasttools::trajectories_to_quantiles(
+    weekly_forecasts,
+    timepoint_cols = c("epiweek", "epiyear"),
+    value_col = "weekly_hosp"
+  ) %>%
+    dplyr::mutate(
+      location = forecasttools::loc_abbr_to_flusight_code(state)
+    ) %>%
+    forecasttools::get_flusight_table(
+      reference_date,
+      horizons = horizons
+    )
+
+  readr::write_csv(formatted_output, output_path)
+  return(formatted_output)
+}
+
+generate_flusight_output <- function(file_paths, reference_date, horizons, output_path) {
+  for (file_path in file_paths) {
+    posterior_samples <- read_posterior_samples(file_path)
+    daily_forecasts <- pivot_forecast_to_long(posterior_samples)
+    weekly_forecasts <- aggregate_to_epiweekly(daily_forecasts)
+    formatted_output <- output_flusight_table(weekly_forecasts, reference_date, horizons, output_path)
+  }
+}
+
+
+file_paths <- c(
+  "posterior_predictive_forecasts_test_NY_2024-01-20.csv")
+reference_date <- "2024-01-20"
+horizons <- -1:3
+output_path <- "flusight_forecast_output_PFL_test_NY_2024-01-20.csv"
+
+generate_flusight_output(
+  file_paths,
+  reference_date,
+  horizons,
+  output_path)
+
+
+
+
+
+
+
+# library(forecasttools)
+# library(tibble)
+# library(dplyr)
+# library(readr)
+# library(lubridate)
+
+# pyrenew_flusight_forecast_from_csv <- function(
+#     spread_draws_csv,
+#     fitting_data_csv,
+#     output_path,
+#     reference_date,
+#     horizons = -1:3,
+#     seed = NULL
+# ) {
+#   set.seed(seed)
+#   # read data from pyrenew-flu-light run
+#   spread_draws <- read_csv(spread_draws_csv)
+#   fitting_data <- read_csv(fitting_data_csv)
+#   # retrieve locations from fitting data
+#   locations <- unique(fitting_data$location)
+#   # parse over and collect forecasts and fitting data
+#   state_daily_forecast_list <- list()
+#   for (loc in locations) {
+#     state_fitting_data <- fitting_data %>% filter(location == loc)
+#     state_forecast <- spread_draws %>% filter(negbinom_rv_dim_0_index == loc)
+#     # forecasttools convert spread draws tiddy
+#     state_forecast_long <- state_forecast %>%
+#       dplyr::mutate(.draw = draw) %>%
+#       dplyr::select(.draw, date = negbinom_rv_dim_0_index, hosp = negbinom_rv)
+#     # go to epiweekly from daily
+#     state_weekly_forecasts <- forecasttools::daily_to_epiweekly(
+#       tidy_daily_trajectories = state_forecast_long,
+#       value_col = "hosp",
+#       date_col = "date",
+#       id_cols = ".draw"
+#     )
+#     state_daily_forecast_list[[loc]] <- state_weekly_forecasts
+#   }
+#   cli::cli_inform("Formatting output for FluSight...")
+#   # flusight formatting
+#   state_flusight_tables <- list()
+#   full_table <- tibble::tibble()
+#   for (state in names(state_daily_forecast_list)) {
+#     state_flusight_table <- forecasttools::trajectories_to_quantiles(
+#       state_daily_forecast_list[[state]],
+#       timepoint_cols = c("epiweek", "epiyear"),
+#       value_col = "weekly_hosp"
+#     ) %>%
+#       dplyr::mutate(
+#         location = forecasttools::loc_abbr_to_flusight_code(state)
+#       ) %>%
+#       forecasttools:::get_flusight_table(
+#         reference_date,
+#         horizons = horizons
+#       )
+
+#     full_table <- dplyr::bind_rows(
+#       full_table,
+#       state_flusight_table
+#     )
+#   }
+#   full_table <- full_table %>%
+#     dplyr::arrange(
+#       location,
+#       reference_date,
+#       horizon,
+#       output_type,
+#       output_type_id
+#     )
+#   # save
+#   readr::write_csv(
+#     full_table,
+#     output_path
+#   )
+#   return(full_table)
+# }
+
+# spread_draws_csv <- "AL_2024-03-30_28_NegBinRv.csv"
+# fitting_data_csv <- "filtered_data_AL.csv"
+# output_path <- "flusight_output_AL.csv"
+# reference_date <- "2024-03-30"
+# horizons <- -1:3
+
+# run_forecast_with_csvs(
+#     spread_draws_csv,
+#     fitting_data_csv,
+#     output_path,
+#     reference_date,
+#     horizons,
+#     seed = 62352
+# )

pyrenew_flu_light/__init__.py

@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 from checks import (
     assert_historical_data_files_exist,
     check_file_path_valid,

pyrenew_flu_light/checks.py

@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 """
 Methods to verify pathing and existence
 of certain files for use of pyrenew-flu-light.
@@ -62,7 +60,7 @@ def load_config(config_path: str) -> dict[str, any]:
     return config
 
 
-def ensure_output_directory(args: dict[str, any]):  # numpydoc ignore=GL08
+def ensure_output_directory(args: dict[str, any]):
     output_directory = "./output/"
     if not os.path.exists(output_directory):
         os.makedirs(output_directory)
@@ -79,7 +77,7 @@ def ensure_output_directory(args: dict[str, any]):  # numpydoc ignore=GL08
 
 def assert_historical_data_files_exist(
     reporting_date: str,
-):  # numpydoc ignore=GL08
+):
     data_directory = f"../model_comparison/data/{reporting_date}/"
     assert os.path.exists(
         data_directory

pyrenew_flu_light/comp_inf.py

@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 """
 The infections process component in pyrenew-flu-light.
 """
@@ -36,7 +34,7 @@ def __init__(
         self,
         I0: ArrayLike,
         susceptibility_prior: numpyro.distributions,
-    ):  # numpydoc ignore=GL08
+    ):
         logging.info("Initializing CFAEPIM_Infections")
 
         self.I0 = I0
@@ -138,7 +136,7 @@ def sample(
         # calculate initial susceptible population S_{v-1}
         init_S = init_S_proportion * P
 
-        def update_infections(carry, Rt):  # numpydoc ignore=GL08
+        def update_infections(carry, Rt):
             S_t, I_recent = carry
 
             # compute raw infections

pyrenew_flu_light/comp_obs.py

@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 """
 The observation process component in pyrenew-flu-light.
 """
@@ -44,7 +42,7 @@ def __init__(
         alpha_prior_dist,
         coefficient_priors,
         nb_concentration_prior,
-    ):  # numpydoc ignore=GL08
+    ):
         logging.info("Initializing CFAEPIM_Observation")
 
         # CFAEPIM_Observation.validate(

pyrenew_flu_light/comp_tran.py

@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 """
 The transmission process (Rt) component in pyrenew-flu-light.
 """
@@ -18,14 +16,14 @@
 from pyrenew.randomvariable import DistributionalVariable, TransformedVariable
 
 
-class CFAEPIM_Rt(RandomVariable):  # numpydoc ignore=GL08
+class CFAEPIM_Rt(RandomVariable):
     def __init__(
         self,
         intercept_RW_prior: numpyro.distributions,
         max_rt: float,
         gamma_RW_prior_scale: float,
         week_indices: ArrayLike,
-    ):  # numpydoc ignore=GL08
+    ):
         """
         Initialize the CFAEPIM_Rt class.
 
@@ -55,7 +53,7 @@ def validate(
         max_rt: any,
         gamma_RW_prior_scale: any,
         week_indices: any,
-    ) -> None:  # numpydoc ignore=GL08
+    ) -> None:
         """
         Validate the parameters of the CFAEPIM_Rt class.
 
@@ -85,7 +83,7 @@ def validate(
                 f"week_indices must be an array-like structure; was type {type(week_indices)}"
             )
 
-    def sample(self, n_steps: int, **kwargs) -> tuple:  # numpydoc ignore=GL08
+    def sample(self, n_steps: int, **kwargs) -> tuple:
         """
         Sample the Rt values using a random walk process
         and broadcast them to daily values.

pyrenew_flu_light/compare.py

@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 """
 Comparisons made between posterior samples.
 """
@@ -18,7 +16,7 @@ def quantilize_forecasts(
     fitting_data,
     output_path,
     reference_date,
-):  # numpydoc ignore=GL08
+):
     pandas2ri.activate()
     forecasttools = importr("forecasttools")
     # dplyr = importr("dplyr")