elex-model 2.0.3

A package for the Washington Post's live election night model

elex-live-model

The Washington Post uses this live election model to generate estimates of the number of outstanding votes on an election night based on the current results of the race. It is agnostic to the quantity that it is estimating. For general elections, we also generate estimates of the partisan split of outstanding votes, and for primaries, we split estimates by candidate.

Generally, the model works by comparing the current results to a historical baseline and regressing on the difference using demographic features. We use quantile regression as the underlying model and conformal prediction to produce our uncertainty estimates.

The first iteration of this model is written in R in this repo.

How to Run the Model

For dev work, skip this section and go on to the Development section below.

Installation

• We recommend that you set up a virtualenv and activate it (IE mkvirtualenv elex-model via http://virtualenvwrapper.readthedocs.io/en/latest/).
• Run pip install elex-model
  • If you get stuck with an error about qdldl (e.g. RuntimeError: CMake must be installed to build qdldl), you are missing cmake. You can install it with Homebrew with brew install cmake or compile it from source.

Usage

We can run the model with a CLI or with Python.

We can use the model to generate current estimates or for a historical evaluation. Historical evaluation means running the "current reporting" subunits with data from a previous election, and then calculating the error that the current set of reporting subunits would have given us. This allows to test how representative the currently reporting subunits are.

See more information on how to pass data to the model in the data README.

CLI

The CLI is for local development and testing purposes only. We cannot run a live election through the CLI because it pulls vote counts from data files located either in S3 or locally. It does not retrieve current data from the Dynamo database of election results.

The CLI takes an election ID, estimands, office ID, and a geographic unit type. If you're running the model with local data files, they should be located at elex-live-model/data/{election_id}/{office_id}/data_{geographic_unit_type}.csv. Otherwise, the model will attempt to find the data files in S3.

Pass in a command like this:

elexmodel 2017-11-07_VA_G --estimands=dem --office_id=G --geographic_unit_type=county

You can also pass in multiple estimands:

elexmodel 2017-11-07_VA_G --estimands=dem --estimands=turnout --office_id=G --geographic_unit_type=county --percent_reporting 40

If you want to run a test with some nonreporting subunits, you can use the --percent_reporting cli parameter:

elexmodel 2017-11-07_VA_G --estimands=dem --office_id=G --geographic_unit_type=county --percent_reporting 40

Historical election

If you want to run a historical election, you can use the --historical flag. For this to succeed, the election must have historical data already prepared.

elexmodel 2021-11-02_VA_G --estimands=dem --office_id=G --geographic_unit_type=county --percent_reporting 60 --historical

Parameters

Parameters for the CLI tool:

Name	Type	Acceptable values
election_id	string	YYYY-MM-DD_{geography}_{election_type} geography is the state or USA and election type is G for general or 'P' for primary
estimands	list	party name (i.e. dem, gop) or turnout in a general; {candidate_last_name}_{polID} in a primary
office_id	string	Presidential (P), Senate (S), House (H), Governor (G), state Senate (Z), state House (Y)
geographic_unit_type	string	county, precinct, county-district, or precinct-district
percent_reporting	numeric	0-100
historical	flag
features	list	features to include in the model
fixed_effects	dict	specified as: {"<fixed_effect_column>: [which fixed effects to include]}; to include all effects for a column, use ["all"]; possible values for the fixed effect variable include: postal_code, county_classification or county_fips, but really any prepared categorical variable
aggregates	list	list of geographies for which to calculate predictions beyond the original postal_code, county_fips, district, county_classification
pi_method	string	method for constructing prediction intervals (nonparametric or gaussian)
model_parameters	dict	dictionary of model specific parameters e.g. --model_parameters='{"robust":True}'
save_output	list	results, data, config
unexpected_units	int	number of unexpected units to simulate; only used for testing and does not work with historical run

Note: When running the model with multiple fixed effects, make sure they are not linearly dependent. For example, county_fips and county_classification are linearly dependent when run together. That's because every county is in one county class, so all the fixed effect columns of the counties in the county class sum up to the fixed effect column of that county class.

Custom Estimands

It's possible to create a custom estimand based on other data elements. Here's how to create a new estimand called "my_estimand":

1. In src/elexmodel/handlers/data/Estimandizer.py, create a function with the signature def my_estimand(data_df).
2. In my_estimand(), use the columns in data_df to create a new column, either baseline_my_estimand or results_my_estimand as necessary. See the party_vote_share_dem function for an example.
3. Specify my_estimand as one of your estimands. For example, via the command line:

elexmodel 2017-11-07_VA_G --estimands my_estimand --office_id=G --geographic_unit_type=county --percent_reporting 50

Your output should have columns including baseline_my_estimand, results_my_estimand, and related columns for the prediction intervals, if using them.

Here's an example showing multiple estimands, including my_estimand:

elexmodel 2017-11-07_VA_G --estimands=turnout --estimands my_estimand --estimands party_vote_share_dem --office_id=G --geographic_unit_type=county --percent_reporting 50

Python

Model Parameters

Some model types have specific model parameters that can be included.

Name	Type	Acceptable values	model
lambda	numeric	regularization constant	all
turnout_factor_lower	numeric	drop units with < turnout factor	all
turnout_factor_upper	numeric	drop units with < turnout factor	all
robust	boolean	larger prediction intervals	nonparametric
beta	numeric	variance inflation	gaussian
winsorize	boolean	winsorize std estimate	gaussian
B	numeric	bootstrap samples	bootstrap
T	numeric	temperature for aggregate	bootstrap
strata	list	groups to stratify bootstrap by	bootstrap
agg_model_hard_threshold	bool	hard threshold aggregate model	bootstrap
y_LB	numeric	lower bound norm. margin dist	bootstrap
y_UB	numeric	upper bound norm. margin dist	bootstrap
z_LB	numeric	lower bound turnout fact dist	bootstrap
z_UB	numeric	lower bound turnout fact dist	bootstrap
y_unobserved_lower_bound	numeric	lower bound for norm. margin	bootstrap
y_unobserved_upper_bound	numeric	upper bound for norm. margin	bootstrap
percent_expected_vote_error_bound	numeric	error tolerance on expected vote	bootstrap
z_unobserved_lower_bound	numeric	lower bound for turnout factor	bootstrap
z_unobserved_upper_bound	numeric	upper bound for turnout factor	bootstrap

This is the class and function that invokes the general function to generate estimates. You can install elex-model as a Python package and use this code snippet in other projects.

from elexmodel.client import ModelClient

model_client = ModelClient()
model_response = model_client.get_estimates(
  current_results,
  election_id,
  office,
  estimand, 
  prediction_intervals,
  percent_reporting_threshold,
  geographic_unit_type,
)

Historical election

This is the class and function that invokes a historical evaluation. You can install elex-model as a Python package and use this code snippet in other projects.

from elexmodel.client import HistoricalModelClient

historical_model_client = HistoricalModelClient()
model_evaluation = historical_model_client.get_historical_evaluation(
  current_data,
  election_id,
  office,
  estimand,
  prediction_intervals,
  percent_reporting_threshold,
  geographic_unit_type
)

Development

We welcome contributions to this repo. Please open a Github issue for any issues or comments you have.

Installation

Clone the repository and install the requirements:

  pip install -r requirements.txt
  pip install -r requirements-dev.txt

Create a .env file in the top directory and add the below variables. Assuming your S3 bucket and path roots are named elex-models, set these as your variables:

  APP_ENV=local
  DATA_ENV=dev
  MODEL_S3_BUCKET=elex-models
  MODEL_S3_PATH_ROOT=elex-models

Testing

• pip install -r requirements-dev.txt
• tox

We also have a requirements-test.txt file which is used for running unit tests only. It is installed automatically as part of installing requirements-dev.txt.

Precommit

To run precommit hooks for linting, run:

pre-commit run --all-files

Release

To release a new version manually:

1. Decide what the next version will be per semantic versioning: X.X.X
2. Make a new branch from develop called release/X.X.X
3. Update the version in setup.py
4. Update the changelog with all the chnages that will be included in the release
5. Commit your updates and open a PR against main
6. Once the PR is merged, tag main (or develop for a beta release) with the version's release number (git tag X.X.X) and push that tag to github (git push --tags)
7. Merge main into develop

Then, we need to release this version to PyPi. This repository has a Github Action workflow that automatically builds and releases the latest version to TestPyPi and PyPi on pushes to main. However, to release to PyPi manually:

1. Generate a distribution archive:
   1. Make sure requirements-dev.txt is installed
   2. Run python3 -m pip install --upgrade build to install build
   3. Run python3 -m build. This should generate two files in the dist/ directory.
   4. Check to make sure the correct version is installed in the dist/ folder that should now exist at the base of the repo folder. If you've previously run these commands locally for an earlier version, you may need to delete the older files in dist/ order to upload them correctly in the next step. You can just delete the entire dist/ folder and run the above command again.
2. Upload the distribution archive:`
   1. Run python3 -m pip install --upgrade twine
   2. Upload to TestPyPi with python3 -m twine upload --repository testpypi dist/*
   3. Upload to PyPi python3 -m twine upload dist/*

Further Reading

We have published multiple resources to share our progress.

• October 2020: "How The Washington Post Estimates Outstanding Votes for the 2020 Presidential Election"
• November 1, 2020: "What the Post’s election results will look like"
• November 2020: Github repository for the original election night model used in the 2020 elections
• December 2020: "An Update To The Washington Post Election Night Model"
• 2020 General election night model open sourced repository
• February 21, 2021: "How The Washington Post Estimates Outstanding Votes for the 2020 Presidential Election"
• November 2, 2021: "How The Washington Post will model possible outcomes in the Virginia governor’s race"
• May 17, 2022: "How the Washington Post’s election night model works"
• September 14, 2022: "We're open sourcing our live election night model"