PredictionIO classification engine for Heroku

Demo engine for the PredictionIO Heroku buildpack. See the 📚 README for how-to.

Based on the attribute-based classifier template, modified to use an alternative algorithm, Random Forests.

Data shape

Service plans are:

• 0 Low Usage: no services significantly utilized
• 1 More Voice: expanded talk time to 1000 minutes
• 2 More Data: expanded transfer quota to 1000 megabytes
• 3 More Texts: expanded SMS to 1000 messages
• 4 Voice + Data: expanded talk time & transfer quota
• 5 Data + Text: expanded transfer quota & SMS
• 6 Voice + Text: expanded talk time & SMS
• 7 More Everything: all services used evenly

PredictionIO Engine Heroku

Run Locally:

1. Start Postgres

2. Set your PredictionIO app's access key and app name in your env vars:

    export ACCESS_KEY=<YOUR ACCESS KEY>
    export APP_NAME=<YOUR APP NAME>
   

   Note: These values come from the apps defined in your event server.

3. Train the app:

    source bin/env.sh && ./sbt "runMain TrainApp"
   

4. Start the server:

    source bin/env.sh && ./sbt "runMain ServerApp"
   

5. Check the status of your engine:

   http://localhost:8000

6. Check out the recommendations for an item:

   Note: Must be an item that has events

    curl -H "Content-Type: application/json" -d '{ "items": ["i11"], "num": 4 }' -k http://localhost:8000/queries.json
   

Run on Heroku:

1. Deploy:

2. Attach your PredictionIO Event Server's Postgres:

    heroku addons:attach YOUR-ADDON-ID
   

   Note: You can find out <YOUR-ADDON-ID> by running: heroku addons -a <YOUR EVENT SERVER HEROKU APP NAME>

3. Configure the Heroku app:

    heroku config:set ACCESS_KEY=<YOUR APP ACCESS KEY> APP_NAME=<APP NAME> EVENT_SERVER_IP=<YOUR EVENT SERVER HOSTNAME> EVENT_SERVER_PORT=80
   

4. Train the app:

    heroku run train
   

5. Restart the app to load the new training data:

    heroku restart