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:

Start Postgres
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.

Train the app:

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

Start the server:

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

Check the status of your engine:

http://localhost:8000

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:

Deploy:
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>

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

Train the app:
```
 heroku run train
```
Restart the app to load the new training data:
```
 heroku restart
```