From 64276d6cf10bffa74d27282f7e6e17054adb813d Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar (UMass)" Date: Fri, 13 Dec 2024 14:52:35 -0800 Subject: [PATCH 1/4] first draft of Evan's proposal --- .../Evaldashboardplanning.html | 1 + .../2024-12-13-planning/images/image1.png | Bin 0 -> 199015 bytes .../2024-12-13-planning/images/image2.png | Bin 0 -> 716878 bytes .../2024-12-13-planning/images/image3.png | Bin 0 -> 109016 bytes .../eval-dashboard/eval-dashboard.md | 132 ++++++++++++++++++ 5 files changed, 133 insertions(+) create mode 100644 project-posters/eval-dashboard/2024-12-13-planning/Evaldashboardplanning.html create mode 100644 project-posters/eval-dashboard/2024-12-13-planning/images/image1.png create mode 100644 project-posters/eval-dashboard/2024-12-13-planning/images/image2.png create mode 100644 project-posters/eval-dashboard/2024-12-13-planning/images/image3.png create mode 100644 project-posters/eval-dashboard/eval-dashboard.md diff --git a/project-posters/eval-dashboard/2024-12-13-planning/Evaldashboardplanning.html b/project-posters/eval-dashboard/2024-12-13-planning/Evaldashboardplanning.html new file mode 100644 index 0000000..a600a25 --- /dev/null +++ b/project-posters/eval-dashboard/2024-12-13-planning/Evaldashboardplanning.html @@ -0,0 +1 @@ +

Eval dashboard planning

Table of Contents

Table of Contents

Goal

Prior art

Options, high level

Proposed UI

Acquiring the score data

Output data that we will compute

Software for end-of-round score computations

Front end/app structure

Goal

Website that can be used to explore forecast performance.

  1. “Relative” scores that use a pairwise tournament approach in which each pair of models is compared on the subset of forecast tasks they produced in common.  See, e.g., the reichlab flusight eval tool linked in next section.
  2. In an interactive tool, when a specific set of models is selected, subset to the forecast tasks the selected models have in common and aggregate scores over those tasks dynamically.  See, e.g., the delphi tool linked in next section.

Prior art

https://reichlab.io/flusight-eval/

https://delphi.cmu.edu/forecast-eval/

Options, high level

Score creation:

  1. R or python script runs at round close to compute scores and save them e.g. in an S3 bucket or on GitHub [R easier as of Dec 2024; existing R tooling for computing scores][a]

The website itself:

  1. R markdown document, quarto document, or similar that pre-computes all tables/figures used to display them.
  1. Note: we’ve tried this before for flusight evals, and were not satisfied with the results: when breaking scores down by location, the eval page took ~30s to load.
  2. Retrieving data based on user selections and re-rendering new content is not possible using this option.
  3. This motivates the following two options, with dynamic data fetches and rendering of web page elements
  1. Front-end Javascript app running in client side browser fetches data as needed and generates plots/etc: similar structure to predtimechart
  2. Web app with server-side logic to render tables and/or figures, e.g. using Shiny or Streamlit
  1. It would likely still make sense to set up scheduled end-of-round computation of scores to cache, as score computation can be too time-consuming to do on the fly in the context of an interactive app.
  1. webR or similar: R running in the browser
  1. This may work, but when we looked in June 2024 it wasn’t ready for prime-time yet.
  2. https://shinylive.io/r/examples/ 
  3. Shifting to using webR from a javascript app at a later date would not require changing the rest of the dashboard structure as a static site.

As noted above, option 1 above has not been performant when we’ve tried it in the past.

From a technical perspective, both options 2 and 3 seem workable.

Option 2 is a better fit with our current dashboard system because Option 3 involves setting up a server to run behind a dashboard (or somehow using someone’s provided hosting) and the current dashboard system involves only static webpages. Therefore it introduces another webhost beyond GitHub Pages.

Additionally, the dashboard is currently using standard libraries so there is less dependency-management involved than with one of the option 3 solutions. The Javascript app would be bundled with a set of dependencies specifically delineated.

A disadvantage with Option 2 is that it creates a front-end Javascript tool that we are responsible for developing and maintaining. The Lab’s “best” language is not Javascript.

Proposed UI

Here are some rough sketches of what this might look like:

Acquiring the score data

At the end of each round, I propose to create and save a set of data objects with pre-computed scores. Subsections below describe what data we will compute and how we will compute it.

Output data that we will compute

Proposal: compute a dataset with scores for each combination of:

For example, for FluSight, in the past we have done evaluations with 2 windows: (1) the full season, and (2) the last 4 reference dates.  FluSight has four targets, and within those, the following options for disaggregating scores by relevant task id variables:

There will be a config file specifying what the evaluation windows to use are and what scores to compute for each of those target/task id combinations.

With those settings, we would create a total of 2 * 16 = 32 files with precomputed scores at the close of each round.  As an example, the structure of the file for one evaluation window, wk inc flu hosp, disaggregated by horizon, might look like this:

model_id

horizon

WIS

Relative WIS

MAE

Relative MAE

50% Coverage

95% Coverage

FluSight-baseline

0

FluSight-baseline

1

FluSight-baseline

2

We could save the files in our favorite place to save data files that need to be accessed by web apps (i.e., github or S3 bucket).

Software for end-of-round score computations

We will build this in R because then we can use the scoring functionality in the hubEvals package.

Inputs:

Outputs:

out_path / target / eval_window / disaggregate_by / scores.csv

Config structure (yaml file):

General plan for R code:

#' Generate evaluation data for a hub

#'

#' @param hub_path A path to the hub. See \code{\link{hubData::hub_path}}.

#' @param webevals_config_path A path to a `webevals-config.yaml` file that

#' specifies the configuration options for the evaluation.

#' @param out_path The directory to write the evaluation data to.

#' @param oracle_output An optional data frame of oracle output to use for the evaluation.  This only needs to be provided if the oracle output is not already stored within the hub.

generate_eval_data <- function(hub_path,

                               webevals_config_path,

                               out_path,

                               oracle_output)

Front end/app structure

Main to-dos, no matter what tooling we use to build this:

If we go ahead with option 2 from the “Options, high level” section, I’d propose to build a javascript single page application using a setup quite similar to predtimechart.

Goals for meeting

[a]Nick: all of these options involve this step

[b]We could also allow for specification of a set of round ids to include, i.e., you type the ones you want to evaluate directly into your config file.

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+++ b/project-posters/eval-dashboard/eval-dashboard.md @@ -0,0 +1,132 @@ +# Project Poster: [project name] + +- Date: 2024-12-13 + +- Owner: Evan L. Ray + +- Team: Zhian N. Kamvar, Evan L. Ray + +- Status: draft + +## ❓ Problem space + +2024-11-13: this is summarized from [the planning document Evan created](2024-12-13-planning/Evaldashboardplanning.html) + +### What are we doing? + +Website that can be used to explore forecast performance. + - Overall performance by model + - Performance broken down by levels of one task id variable (e.g. by location, by reference date, by horizon, by target date) + - Scores may be represented using: + - Tables + - Line plots: one line per model showing scores over time, scores by location, etc. + - Heat maps: fill color corresponds to score value, models in rows or columns, task id variable values in columns or rows + - Key question: how to handle missing values (e.g. a team submits only for California, or teams submit for different subsets of dates). + - Past work (see next section) has used 2 main strategies: + - “Relative” scores that use a pairwise tournament approach in which + each pair of models is compared on the subset of forecast tasks they + produced in common. See, e.g., the reichlab flusight eval tool + linked in next section. + - In an interactive tool, when a specific set of models is selected, + subset to the forecast tasks the selected models have in common and + aggregate scores over those tasks dynamically. See, e.g., the + delphi tool linked in next section. + - The first of these options is substantially easier from a technical perspective, so I (Evan) put in a strong vote for that one. + +### Why are we doing this? + +TODO: We needs + +### What are we _not_ trying to do? + +Anti-aim: more than one task id variable is actually an anti-aim for now because it's more complex, but in practice it's what is normally done + +### How do we judge success? + +A list of outcomes that should be true for the project to be considered a +success. Be sure to focus on outcomes, not implementation details. + +### What are possible solutions? + +NOTE: A detailed [writeup can be found as an HTML document in this folder](2024-12-13-planning/Evaldashboard.html). + +#### Score Creation + +1. R or python script runs at round close to compute scores and save them e.g. in an S3 bucket or on GitHub (R easier as of Dec 2024; existing R tooling for computing scores). + +#### The website itself + +1. R markdown document, quarto document, or similar that pre-computes all + tables/figures used to display them. + - Note: we’ve tried this before for flusight evals, and were not satisfied + with the results: when breaking scores down by location, the eval page took + ~30s to load. + - Retrieving data based on user selections and re-rendering new content is + not possible using this option. + - This motivates the following two options, with dynamic data fetches and + rendering of web page elements +2. Front-end Javascript app running in client side browser fetches data as + needed and generates plots/etc: similar structure to predtimechart +3. Web app with server-side logic to render tables and/or figures, e.g. using + Shiny or Streamlit + - It would likely still make sense to set up scheduled end-of-round + computation of scores to cache, as score computation can be too + time-consuming to do on the fly in the context of an interactive app. +4. webR or similar: R running in the browser + - This may work, but when we looked in June 2024 it wasn’t ready for + prime-time yet. + - https://shinylive.io/r/examples/ + - Shifting to using webR from a javascript app at a later date would not + require changing the rest of the dashboard structure as a static site. + +## ✅ Validation + +### What do we already know? + +Prior art in two dashboards: one static, the other server-generated + + - https://reichlab.io/flusight-eval/ + - https://delphi.cmu.edu/forecast-eval/ + +The static dashboard is built from R Markdown and runs pretty slow (and is quite +large due to embedded elements). + + +### What do we need to answer? + +From [Output data that we will compute](2024-12-13-planning/Evaldashboard.html#h.gdnhn91qg7og): + + - Note, we may also need to say what output types to use to compute scores? + E.g., for wk inc flu hosp, forecasts are submitted in both quantile and + sample format. We might compute WIS and MAE based on the quantile + forecasts, and energy score based on the sample forecasts? + - It may be possible to infer the desired output type to use for computing + scores based on the name of the score; propose to do nothing about this + until we realize it won’t work. + - For energy score, do we need to specify what the compound unit is? This + gets complicated, and is a bridge we don’t have to cross quite yet. + +## 👍 Ready to make it + +If, after defining the problem space and validating assumptions, the team +decides to move forward with the project, this section is to document +a proposed solution. + +Keep the answers to the prompts below brief--this document isn't +inteded to be a detailed project plan. + +### Proposed solution + +1-2 sentences about the team's chosen approach for tackling the problem space. + +### Visualize the solution + +Optional. If there is a mockup, data dictionary, or other artifact that clarifies +the project's implementation, you can put it here (or link to it). + +### Scale and scope + +A place to define items like: + +- Required team size +- Required delivery date (if applicable) From c763559caa640df5143daf71de4171828e3f294c Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar (UMass)" Date: Fri, 13 Dec 2024 14:55:52 -0800 Subject: [PATCH 2/4] link document to commit id --- project-posters/eval-dashboard/eval-dashboard.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/project-posters/eval-dashboard/eval-dashboard.md b/project-posters/eval-dashboard/eval-dashboard.md index a0f219f..6a57813 100644 --- a/project-posters/eval-dashboard/eval-dashboard.md +++ b/project-posters/eval-dashboard/eval-dashboard.md @@ -10,7 +10,7 @@ ## ❓ Problem space -2024-11-13: this is summarized from [the planning document Evan created](2024-12-13-planning/Evaldashboardplanning.html) +2024-11-13: this is summarized from [the planning document Evan created](https://htmlpreview.github.io/?https://github.com/reichlab/decisions/blob/64276d6cf10bffa74d27282f7e6e17054adb813d/project-posters/eval-dashboard/2024-12-13-planning/Evaldashboardplanning.html) ### What are we doing? @@ -48,7 +48,7 @@ success. Be sure to focus on outcomes, not implementation details. ### What are possible solutions? -NOTE: A detailed [writeup can be found as an HTML document in this folder](2024-12-13-planning/Evaldashboard.html). +NOTE: A detailed [writeup can be found as an HTML document in this folder](https://htmlpreview.github.io/?https://github.com/reichlab/decisions/blob/64276d6cf10bffa74d27282f7e6e17054adb813d/project-posters/eval-dashboard/2024-12-13-planning/Evaldashboard.html). #### Score Creation @@ -94,7 +94,7 @@ large due to embedded elements). ### What do we need to answer? -From [Output data that we will compute](2024-12-13-planning/Evaldashboard.html#h.gdnhn91qg7og): +From [Output data that we will compute](https://htmlpreview.github.io/?https://github.com/reichlab/decisions/blob/64276d6cf10bffa74d27282f7e6e17054adb813d/project-posters/eval-dashboard/2024-12-13-planning/Evaldashboard.html#h.gdnhn91qg7og): - Note, we may also need to say what output types to use to compute scores? E.g., for wk inc flu hosp, forecasts are submitted in both quantile and From 152a5fbf26158751ddcb557b1a11835a75eed6e8 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar (UMass)" Date: Fri, 13 Dec 2024 15:23:07 -0800 Subject: [PATCH 3/4] add ldr for eval dashboard structure --- ...2024-11-13-ldr-eval-dashboard-structure.md | 79 +++++++++++++++++++ .../eval-dashboard/eval-dashboard.md | 30 +++---- 2 files changed, 94 insertions(+), 15 deletions(-) create mode 100644 decisions/2024-11-13-ldr-eval-dashboard-structure.md diff --git a/decisions/2024-11-13-ldr-eval-dashboard-structure.md b/decisions/2024-11-13-ldr-eval-dashboard-structure.md new file mode 100644 index 0000000..070ccbd --- /dev/null +++ b/decisions/2024-11-13-ldr-eval-dashboard-structure.md @@ -0,0 +1,79 @@ +# 2024-12-13 Evals Dashboard Underlying Architecture + +## Context + +We need to produce a dashboard for model evaluations for both the FluSight and +COVID forecast hubs. The first decision that we need to make is the question +about what technologies we can use to make the dashboard. + +We have made a promise that the dashboard would be available next year to both +groups. + +There is uncertainty with respect to the funding situation for the development +and maintenance of tools in the lab due to the results of the 2024 US Elections. + +It is important to choose a toolset for this dashboard that can be maintained +under a future where members are fluent in R, but may not be comfortable with +JavaScript. + +### Aims + + - Define the underlying architecture for the hub evals dashboard + +### Anti-Aims + + - Define the behaviour of the dashboard + +## Decision + +Regardless of our choice of system, we will have an R script that pre-computes +data for the tables and figures and store them on a git branch or s3 bucket. + +We will build the dashboard as a front-end javascript application similar to +predtimechart to align with the current status of the hub dashboard project. + +### Other Options Considered + +1. R markdown document, quarto document, or similar that pre-computes all + tables/figures used to display them. + - Note: we’ve tried this before for flusight evals, and were not satisfied + with the results: when breaking scores down by location, the eval page took + ~30s to load. + - Retrieving data based on user selections and re-rendering new content is + not possible using this option. + - **Not chosen because of the above limitations of inflexibility** +2. Web app with server-side logic to render tables and/or figures, e.g. using + Shiny or Streamlit + - It would likely still make sense to set up scheduled end-of-round + computation of scores to cache, as score computation can be too + time-consuming to do on the fly in the context of an interactive app. + - Streamlit serves ads and shiny.io servers are limited by the number of + minutes website visitors use. + - **Not chosen because this requires server-side logic that may not be + compatible with a static site as the current hub dashboard project builds + on.** +3. webR or similar: R running in the browser + - This may work, but when we looked in June 2024 it wasn’t ready for + prime-time yet. + - https://shinylive.io/r/examples/ + - NOTE: Shifting to using webR from a javascript app at a later date would not + require changing the rest of the dashboard structure as a static site. + - **Not chosen because it is still undergoing active development and we may + be able to switch to it later.** + +## Status + +proposed + +## Consequences + + - We will be able to continue movement on this project + - The lab's "best" language is R, so this may create an additional dependency + burden + - Other lab members can be consulted for advice on JavaScript frontend + applications + - Server-side dependency management is not necessary + +## Projects + + - [2024-12: Evals Dashboard](../project-posters/evals-dashboard/evals-dashboard.md) diff --git a/project-posters/eval-dashboard/eval-dashboard.md b/project-posters/eval-dashboard/eval-dashboard.md index 6a57813..2456dfa 100644 --- a/project-posters/eval-dashboard/eval-dashboard.md +++ b/project-posters/eval-dashboard/eval-dashboard.md @@ -58,26 +58,26 @@ NOTE: A detailed [writeup can be found as an HTML document in this folder](https 1. R markdown document, quarto document, or similar that pre-computes all tables/figures used to display them. - - Note: we’ve tried this before for flusight evals, and were not satisfied - with the results: when breaking scores down by location, the eval page took - ~30s to load. - - Retrieving data based on user selections and re-rendering new content is - not possible using this option. - - This motivates the following two options, with dynamic data fetches and - rendering of web page elements + - Note: we’ve tried this before for flusight evals, and were not satisfied + with the results: when breaking scores down by location, the eval page took + ~30s to load. + - Retrieving data based on user selections and re-rendering new content is + not possible using this option. + - This motivates the following two options, with dynamic data fetches and + rendering of web page elements 2. Front-end Javascript app running in client side browser fetches data as needed and generates plots/etc: similar structure to predtimechart 3. Web app with server-side logic to render tables and/or figures, e.g. using Shiny or Streamlit - - It would likely still make sense to set up scheduled end-of-round - computation of scores to cache, as score computation can be too - time-consuming to do on the fly in the context of an interactive app. + - It would likely still make sense to set up scheduled end-of-round + computation of scores to cache, as score computation can be too + time-consuming to do on the fly in the context of an interactive app. 4. webR or similar: R running in the browser - - This may work, but when we looked in June 2024 it wasn’t ready for - prime-time yet. - - https://shinylive.io/r/examples/ - - Shifting to using webR from a javascript app at a later date would not - require changing the rest of the dashboard structure as a static site. + - This may work, but when we looked in June 2024 it wasn’t ready for + prime-time yet. + - https://shinylive.io/r/examples/ + - NOTE: Shifting to using webR from a javascript app at a later date would not + require changing the rest of the dashboard structure as a static site. ## ✅ Validation From f66895491a4eb632d6274972026326492cce0928 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar (UMass)" Date: Fri, 13 Dec 2024 16:07:35 -0800 Subject: [PATCH 4/4] fix file name --- ...rd-structure.md => 2024-12-13-ldr-eval-dashboard-structure.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename decisions/{2024-11-13-ldr-eval-dashboard-structure.md => 2024-12-13-ldr-eval-dashboard-structure.md} (100%) diff --git a/decisions/2024-11-13-ldr-eval-dashboard-structure.md b/decisions/2024-12-13-ldr-eval-dashboard-structure.md similarity index 100% rename from decisions/2024-11-13-ldr-eval-dashboard-structure.md rename to decisions/2024-12-13-ldr-eval-dashboard-structure.md