Subreddit Algebra

A frontend to 538's analysis of subreddit similarity.

Methodology

538 has some really interesting commentary at the end of their article on their methodology.

For convenience and personal familiarity, this ports the R script used by 538 to Python. This tweaks the methodology so as to be able to more efficiently query for nearest neighbors using an index. Cosine Similarity is not a metric space. This exploits the (hopefully accurate) fact that for unit vectors, Euclidean distance is correlated with the value of Cosine Similarity.

With this in mind, this normalizes all feature vectors to unit length, and builds a Ball Tree index for efficient K-Nearest-Neighbors querying.

Installation

This requires running two development servers, one for the Flask based API which integrates with our pickled sklearn models, and another for the create-react-app based frontend.

Frontend

Make sure you have nodejs installed.

cd frontend
npm install # or yarn install
cd frontend
npm start # or yarn start
# you should be automatically sent to localhost:5000 in the browser.

Backend

Building the models is still a manual process of executing SQL code, downloading the results, and using a python script to massage, index, and pickle the results.

Make sure you have pipenv installed and run pipenv install

Query

Follow the instructions in the bigquery README to set execute and download the required file.

Index

With your query results on disk

mkdir output
pipenv run python subreddit_algebra_app/algebra/build_index.py <path_to_table_csv>

This will automatically run the algorithm and processing steps, and save all required data into the output. folder at the root of the project.

Now you can see it in action!

FLASK_APP=subreddit_algebra_app/server.py flask run
curl http://localhost:5000/algebra/highqualitygifs/-/reactiongifs

API

/algebra/<subreddit_1>/<operator/<subreddit_2> - return the closest five subreddits to result of adding or subtracting subreddit_1 and subreddit_2

/completions/<prefix> - return first 10 subreddit names that start with prefix

Load Testing

We use locust. To test, go to the root of the project, and run

locust --host=example.com # replace example.com with the URL of the instance you want to test

Deployment

This project is configured to deploy to AWS Elastic Beanstalk using the eb command line tool.

Upload your pickled models to a bucket in S3, e.g.

aws s3 sync output/ s3://path/to/your/bucket/ # replace with your bucket

You'll have to customize just a little - change the S3_DATA_BUCKET variable in .ebextensions/00_main.config to the S3 bucket associated with your ELB setup.

You'll also want to set the REACT_APP_GA_TRACKING_CODE environment variable in your ELB production environment.

eb setenv REACT_APP_GA_TRACKING_CODE=XXXXXXXXXX # Replace with your GA tracking code

You can then just use the normal commands (eb create, eb deploy).

License

MIT

Contributing

Contributions ✍ are welcome