ndjido / predictionio-template-recommendation-sparklingwater Goto Github PK

This engine template has integrated Sparkling Water's Deep Learning Model by default.

This Engine Template demonstrates an energy forecasting engine. It integrates Deep Learning from the Sparkling Water library to perform energy analysis. We can query the circuit and time, and return predicted energy usage.

Event Data Requirements

By default, the engine requires the following events to be collected:

• Circuit ID
• Time
• Energy

Input Query

• Circuit ID
• Time

Output PredictedResult

• Energy Consumption

Dataset Format

Your data should be in csv format, with the following constraints:

• Row 0 of the dataset must contain integers representing Circuit IDs.
  
• Column 0 of the dataset must contain integers representing Time.
  
• All other rows and columns should contain integers or doubles representing Energy data. Empty cells are ignored.

The file data/sample_data.csv is included for reference.

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

git clone https://github.com/BensonQiu/predictionio-template-recommendation-sparklingwater

$ 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.

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]

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": 1
    }
  },
  ...

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.

To do a sample query, run python query.py from the root directory of your engine. Customize the query by modifying the JSON { circuitId: 1, time: "1422985500" } in query.py. The engine will return a JSON object containing predicted energy usage.