By default, the classification template reads 4 properties of a user entity: "attr0", "attr1", "attr2" and "plan". You can modify the default DataSource to read to read your custom properties or different Entity Type.

In this example, we modify DataSource to read properties "featureA", "featureB", "featureC", "featureD" and "label" for entity type "item".

Note: you also need import events with these properties accordingly.

Modify the readTraining() in DataSource.scala:

  • modify the entityType parameter
  • modify the list of properties names in the required parameter
  • modify how to create the LabeledPoint object using the entity properties
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  def readTraining(sc: SparkContext): TrainingData = {
    ...
    val labeledPoints: RDD[LabeledPoint] = eventsDb.aggregateProperties(
      appId = dsp.appId,
      entityType = "item", // MODIFFIED HERE
      required = Some(List( // MODIFIED HERE
        "featureA", "featureB", "featureC", "featureD", "label")))(sc)
      // aggregateProperties() returns RDD pair of
      // entity ID and its aggregated properties
      .map { case (entityId, properties) =>
        try {
          // MODIFIED HERE
          LabeledPoint(properties.get[Double]("label"),
            Vectors.dense(Array(
              properties.get[Double]("featureA"),
              properties.get[Double]("featureB"),
              properties.get[Double]("featureC"),
              properties.get[Double]("featureD")
            ))
          )
        } catch {
          case e: Exception => {
            logger.error(s"Failed to get properties ${properties} of" +
              s" ${entityId}. Exception: ${e}.")
            throw e
          }
        }
      }
    ...
  }

Lastly, redefine the Query class parameters to take in four double values: featureA, featureB, featureC, and featureD. Now, to send a query, the field names must be changed accordingly:

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$ curl -H "Content-Type: application/json" -d '{ "featureA":2, "featureB":0, "featureC":0, "featureD":0 }' http://localhost:8000/queries.json

That's it! Now your classifcation engine is using different properties as training data.