PredictionIO AutoML Engine Template

This is a Apache PredictionIO engine template which offers AutoML capability using TransmogrifAI.

You can launch a prediction WebAPI service without any coding.

Prerequisites

• Apache PredictionIO 0.14.0

• Apache Spark 2.3.2

• Java 1.8

• TransmogrifAI 0.6.0

• Scala 2.11.12

• Make sure you compile PredictionIO with the correct scala & spark version (check out detailed instructions):

  $ ./make-distribution.sh -Dscala.version=2.11.12 -Dspark.version=2.3.2

NOTE: if the compilation fails due to cache problems, you may want to manually remove ~/.ivy2 folder and try again.

Run Titanic example

Create an application.

$ pio app new MyAutoMLApp1
[INFO] [App$] Initialized Event Store for this app ID: 4.
[INFO] [Pio$] Created a new app:
[INFO] [Pio$]       Name: MyAutoMLApp1
[INFO] [Pio$]         ID: 1
[INFO] [Pio$] Access Key: xxxxxxxxxxxxxxxx

Set the accesskey to an environmental variable.

$ export ACCESS_KEY=xxxxxxxxxxxxxxxx

Run the event server.

$ pio eventserver &

Import data to the event server.

$ python ./data/import_titanic.py --file ./data/titanic.csv --access_key $ACCESS_KEY

Build the app

$ pio build --verbose

Train a model. It can take a long time to find the best model.

$ pio train

Deploy the trained model as Web API.

$ pio deploy

Test the Web API.

$ curl -H "Content-Type: application/json" -d '{ "pClass": "2", "name": "Wheadon, Mr. Edward H", "sex": "male", "age": 66, "sibSp": 0, "parCh": 0, "ticket": "C.A 24579", "fare", 10.5, "cabin": "", "embarked": "S" }' http://localhost:8000/queries.json
{"survived":0.0}

$ curl -H "Content-Type: application/json" -d '{ "pClass": "2", "name": "Nicola-Yarred, Miss. Jamila", "sex": "female", "age": 14, "sibSp": 1, "parCh": 0, "ticket": "2651", "fare", 11.2417, "cabin": "", "embarked": "C" }' http://localhost:8000/queries.json
{"survived":1.0}

Customize

You only need to modify algorithm parameters in engine.json to customize this template.

"algorithms": [
  {
    "name": "algo",
    "params": {
      "target" : "survived",
      "schema" : [
        {
          "field": "survived",
          "type": "double",
          "nullable": false
        },
        {
          "field": "pClass",
          "type": "string",
          "nullable": true
        },
        ...
      ]
    }
  }
]

Define schema according to your data, and specify target which will be a response of prediction Web API. Note that the target field type must be double for now.