This template shows example usage of the Metis Machine platform for the purpose of building a recommendation engine model. Given a dataset of user IDs, item IDs, and ratings (either boolean or range), this template allows the user to get item recommendations for each user stored in a separate table. Here we use user votes (thumbs up and down) on various movies as the example use case.
- User must have python 3.6
- User must have skafos CLI and skafos SDK installed (
brew install skafos) - User must have git installed and a github account created --> https://git-scm.com
- movies
__init__.pyconstants.pylogger.pyhelpers.py
- metis.config.yml
- environment.yml
- README.md
- engine.py
- The files in movies contain helper functions, various constants (table schemas), and a logger.
- engine.py is the main script that runs for this task. It performs the following:
- Generates fake user-movie votes and stores them in a table (unless a keyspace is provided).
- Pulls in votes data from table and does some reshaping and cleaning.
- Trains a recommender model using Explicit Matrix Factorization (using PyTorch based implementation called Spotlight)
- Generates movie recommendations for each user
These are the environment variables that could be set from the terminal in the project directory using the Skafos CLI.
optional
- BATCH_SIZE, default=10: Number of rows written to the database at a time.
- KEYSPACE, default=None: Namespace in cassandra if the user has a set of tables to use that are pre-built.
Once the user is ready to fire it off (after following the dependency steps above):
- CREATE: a github repository and attach it to the skafos app here --> https://github.com/apps/skafos
- OPEN: one of the files and make some sort of change (add a comment, add new functionality, change config file, etc)
- From Project Directory RUN:
$ git init
$ git add .
$ git commit -am "<message>"
$ git remote add origin git@github.com:<organization>/<repository-name>.git