OpenNLP Text Category Classifier Engine Template

This engine template has integrated OpenNLP's GISModel for text classification.

Overview

This engine template utilizes the GIS algorithm from the Apache OpenNLP library to classify text based off of training data.

Versions

v0.1.0

• initial version

Usage

Event Data Requirements

Input Query

• Phrase
• Category

Output PredictedResult

• Category

Dataset Format Training Data: Your training data should be a single line with a sentence and a category seperated by a tab. *Note that all words should have a single space between them. For example,

Sports	Russell Wilson is a super bowl quarterback	

1. Run PredictionIO

If PredictionIO is not installed, install it here.

Start all components (Event Server, Elaticsearch, and HBase).

Note: If pio-start-all is not recognized, upgrade to the latest version of PredictionIO.

$ pio-start-all

Verify the status of components:

$ pio status

2. Download the Engine Template

git clone ....FILL IN LATER....

3. Create a new application

$ pio app new [YourAppName]

The console output should include the App Name, App ID, and Access Key. You will need the App ID and Access Key in future steps. You can view your applications by entering pio app list.

4. Import Data to the Event Server

Install the PredictionIO Python SDK:

$ pip install predictionio

or

$ easy_install predictionio

From the root directory of your engine, run:

$ python data/import_eventserver.py --access_key [YourAccessKeyFromStep3] --file [/path/to/your/data]

5. Build, Train, and Deploy the Engine

From the root directory of your engine, find engine.json and verify that the appId matches the App Id of your application from Step 3.

 ...
  "datasource": {
    "params" : {
      "appId": App id from step 3 here
    }
  },
  ...

Build the engine.

$ pio build

Train the engine. This may take several minutes.

$ pio train

Deploy the engine. This may take several minutes.

$ pio deploy

After deploying successfully, you can view the status of your engine at http://localhost:8000.

6. Using the Engine

To do a sample query, run python send_data.py from the root directory of your engine. Customize the query by modifying the JSON "sentence" : "Seattle Seahawks" in send_data.py. The engine will return a JSON object containing predicted energy usage.