Release 7.2.0

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 7.1.0
+current_version = 7.2.0
 commit = False
 tag = False
 

.github/workflows/create_release.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Check out code
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
         with:
           ref: main
           ssh-key: ${{ secrets.CMU_DELPHI_DEPLOY_MACHINE_SSH }}

.github/workflows/release_main.yml

@@ -20,7 +20,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Check out code
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
       - name: Set up Python 3.8
         uses: actions/setup-python@v2
         with:
@@ -49,7 +49,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Check out code
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
         with:
           ref: dev
           fetch-depth: 0

DESCRIPTION

@@ -1,9 +1,9 @@
 Package: forecasteval
 Title: Forecast Evaluation Dashboard
-Version: 7.1.0
+Version: 7.2.0
 Authors@R: c(person("Kate", "Harwood", role = "aut"),
 	   person("Chris", "Scott", role = "ctb"),
-	   person("Jed", "Grabman", role = "ctb")),
+	   person("Jed", "Grabman", role = "ctb"),
        person("Nat", "DeFries", email= "[email protected]", role = c("aut", "cre")))
 Description: This app collects and scores COVID-19 forecasts submitted to the CDC, and displays the results in an RShiny dashboard.
 License: MIT License, Copyright (c) 2021 Delphi contributors

Makefile

@@ -5,6 +5,15 @@ PWD=$(shell pwd)
 S3_URL=https://forecast-eval.s3.us-east-2.amazonaws.com
 S3_BUCKET=s3://forecast-eval
 
+# Change `imageTag` during `make` call via `make <command> imageTag=<tag name>`
+#
+# `imageTag` specifies the tag to be used for the production dashboard Docker
+# image. If building from `main`, it should be `latest`. If building from
+# `dev`, it should be `dev`. The default value used here is meant to prevent
+# the actual `latest` and `dev` images in the image repository from being
+# accidentally overwritten.
+imageTag=local
+
 build: build_dashboard
 
 # Build a docker image suitable for running the scoring pipeline
@@ -66,10 +75,14 @@ build_dashboard_dev: pull_data
 start_dashboard: build_dashboard_dev
 	docker run --rm -p 3838:80 ghcr.io/cmu-delphi/forecast-eval:latest
 
-# Build a docker image for production use
+# Build a docker image for production use. Currently this isn't used anywhere,
+# but could be useful if we need to manually build a docker image for
+# production.
 build_dashboard: pull_data
 	docker build --no-cache=true --pull -t ghcr.io/cmu-delphi/forecast-eval:$(imageTag) -f devops/Dockerfile .
 
-# Push a production docker image to the image repository
+# Push a production docker image to the image repository. Currently this isn't
+# used anywhere, but could be useful if we need to manually release a docker
+# image for production.
 deploy_dashboard: build_dashboard
 	docker push ghcr.io/cmu-delphi/forecast-eval:$(imageTag)

app/R/exportScores.R

@@ -5,7 +5,7 @@ exportScoresUI <- function(id = "exportScores") {
   )
 }
 
-createExportScoresDataFrame <- function(scoreDf, targetVariable, scoreType, forecasters, loc, coverageInterval) {
+createExportScoresDataFrame <- function(scoreDf, targetVariable, scoreType, forecasters, loc, coverageInterval, filterDate) {
   scoreDf <- filter(
     scoreDf[[targetVariable]],
     forecaster %chin% forecasters
@@ -16,7 +16,7 @@ createExportScoresDataFrame <- function(scoreDf, targetVariable, scoreType, fore
     if (targetVariable == "Hospitalizations") {
       scoreDf <- filterHospitalizationsAheads(scoreDf)
     }
-    scoreDf <- filterOverAllLocations(scoreDf, scoreType)
+    scoreDf <- filterOverAllLocations(scoreDf, scoreType, filterDate = filterDate)
     return(scoreDf[[1]])
   } else {
     scoreDf <- filter(scoreDf, geo_value == tolower(loc))

app/assets/about.md

@@ -127,3 +127,18 @@ stateCases = tryCatch(
 ```
 
 
+
+##### Forecasts with actuals
+
+If you are interested in getting the forecasts paired with the corresponding actual values (if you were e.g. testing different evaluation methods), that can be found in [the Amazon S3 bucket](https://forecast-eval.s3.us-east-2.amazonaws.com/) in 3 zip files.
+These files are static, generated using [the aggregation script](https://raw.githubusercontent.com/cmu-delphi/forecast-eval/main/app/assets/forecastsWithActuals.R), and forecast and actual data available on June 12, 2023. The latest forecast date available for each target signal is
+
+* [cases](https://forecast-eval.s3.us-east-2.amazonaws.com/cases.zip): 2023-02-13
+* [hospitalizations](https://forecast-eval.s3.us-east-2.amazonaws.com/hospitalizations.zip):
+  * 1 week: 2023-06-05
+  * 2 week: 2023-06-05
+  * 3 week: 2023-06-05
+  * 4 week: 2023-06-05
+* [deaths](https://forecast-eval.s3.us-east-2.amazonaws.com/deaths.zip): 2023-03-06
+
+If the S3 bucket is down, these files are also available on [Delphi's file-hosting site](https://www.cmu.edu/delphi-web/forecast-eval-scores).

app/assets/forecastsWithActuals.R

@@ -0,0 +1,98 @@
+library(dplyr)
+library(tidyr)
+library(aws.s3)
+
+Sys.setenv("AWS_DEFAULT_REGION" = "us-east-2")
+s3bucket <- tryCatch(
+  {
+    get_bucket(bucket = "forecast-eval")
+  },
+  error = function(e) {
+    e
+  }
+)
+
+readbucket <- function(name) {
+  tryCatch(
+    {
+      s3readRDS(object = name, bucket = s3bucket)
+    },
+    error = function(e) {
+      e
+    }
+  )
+}
+
+# Cases, deaths, hosp scores: needed for "actual"s
+cases <- bind_rows(
+  readbucket("score_cards_nation_cases.rds"),
+  readbucket("score_cards_state_cases.rds")
+)
+deaths <- bind_rows(
+  readbucket("score_cards_nation_deaths.rds"),
+  readbucket("score_cards_state_deaths.rds")
+)
+hosp <- bind_rows(
+  readbucket("score_cards_nation_hospitalizations.rds"),
+  readbucket("score_cards_state_hospitalizations.rds")
+)
+
+# The big one: predictions from all forecasters
+pred <- readbucket("predictions_cards.rds")
+
+# Cases
+pred_cases <- pred %>%
+  filter(signal == "confirmed_incidence_num") %>%
+  mutate(signal = NULL, data_source = NULL, incidence_period = NULL) %>%
+  pivot_wider(
+    names_from = quantile,
+    values_from = value,
+    names_prefix = "forecast_"
+  )
+
+actual_cases <- cases %>%
+  select(ahead, geo_value, forecaster, forecast_date, target_end_date, actual)
+
+joined_cases <- left_join(pred_cases, actual_cases)
+sum(is.na(actual_cases$actual)) == sum(is.na(joined_cases$actual))
+write.csv(joined_cases, "cases.csv")
+
+# Deaths
+pred_deaths <- pred %>%
+  filter(signal == "deaths_incidence_num") %>%
+  mutate(signal = NULL, data_source = NULL, incidence_period = NULL) %>%
+  pivot_wider(
+    names_from = quantile,
+    values_from = value,
+    names_prefix = "forecast_"
+  )
+
+actual_deaths <- deaths %>%
+  select(ahead, geo_value, forecaster, forecast_date, target_end_date, actual)
+
+joined_deaths <- left_join(pred_deaths, actual_deaths)
+sum(is.na(actual_deaths$actual)) == sum(is.na(joined_deaths$actual))
+write.csv(joined_deaths, "deaths.csv")
+
+# Hospitalizations: break up by weeks since we run into memory errors o/w!
+pred_hosp <- actual_hosp <- joined_hosp <- vector(mode = "list", length = 4)
+for (k in 1:4) {
+  cat(k, "... ")
+  days <- (k - 1) * 7 + 1:7
+  pred_hosp[[k]] <- pred %>%
+    filter(signal == "confirmed_admissions_covid_1d", ahead %in% days) %>%
+    mutate(signal = NULL, data_source = NULL, incidence_period = NULL) %>%
+    pivot_wider(
+      names_from = quantile,
+      values_from = value,
+      names_prefix = "forecast_"
+    )
+
+  actual_hosp[[k]] <- hosp %>%
+    filter(ahead %in% days) %>%
+    select(ahead, geo_value, forecaster, forecast_date, target_end_date, actual)
+
+  joined_hosp[[k]] <- left_join(pred_hosp[[k]], actual_hosp[[k]])
+  cat(sum(is.na(actual_hosp[[k]]$act)) == sum(is.na(joined_hosp[[k]]$act)))
+  write.csv(joined_hosp[[k]], sprintf("hospitalizations_%iwk.csv", k))
+}

app/global.R

@@ -10,7 +10,7 @@ library(tsibble)
 library(covidcast)
 library(data.table)
 
-appVersion <- "7.1.0"
+appVersion <- "7.2.0"
 
 COVERAGE_INTERVALS <- c("10", "20", "30", "40", "50", "60", "70", "80", "90", "95", "98")
 CASES_DEATHS_TARGET_DAY <- "Saturday"

app/server.R

@@ -991,7 +991,8 @@ server <- function(input, output, session) {
     "exportScores", shiny::reactive(generateExportFilename(input)),
     shiny::reactive(createExportScoresDataFrame(
       df_list, input$targetVariable, input$scoreType, input$forecasters,
-      input$location, input$coverageInterval
+      input$location, input$coverageInterval,
+      filterDate = dataCreationDate
     ))
   )
 }