How to create a custom transformer with out any input column? - mleap

Question

We have requirement where in we wanted to generate scores of our model with some random values in between 0-1.
To do that we wanted to have a custom transformer which will be generating random numbers with out any input fields.
So can we generate a transformer without input fields in mleap?
Like usually we do create as below:
import ml.combust.mleap.core.Model
import ml.combust.mleap.core.types._
case class RandomNumberModel() extends Model {
private val rnd = scala.util.Random
def apply(): Double = rnd.nextFloat
override def inputSchema: StructType = StructType("input" -> ScalarType.String).get
override def outputSchema: StructType = StructType("output" -> ScalarType.Double ).get
}
How to make it as input schema no necessary to put?

Answer 1

I have never tried that, but given how I achieved to have a custom transformer with multiple input fields ...
package org.apache.spark.ml.feature.mleap
import ml.combust.mleap.core.Model
import ml.combust.mleap.core.types._
import org.apache.spark.ml.linalg._
case class PropertyGroupAggregatorBaseModel (props: Array[String],
aggFunc: String) extends Model {
val outputSize = props.size
//having multiple inputs, you will have apply with a parameter Seq[Any]
def apply(features: Seq[Any]): Vector = {
val properties = features(0).asInstanceOf[Seq[String]]
val values = features(1).asInstanceOf[Seq[Double]]
val mapping = properties.zip(values)
val histogram = props.foldLeft(Array.empty[Double]){
(acc, property) =>
val newValues = mapping.filter(x => x._1 == property).map(x => x._2)
val newAggregate = aggFunc match {
case "sum" => newValues.sum.toDouble
case "count" => newValues.size.toDouble
case "avg" => (newValues.sum / Math.max(newValues.size, 1)).toDouble
}
acc :+ newAggregate
}
Vectors.dense(histogram)
}
override def inputSchema: StructType = {
//here you define the input
val inputFields = Seq(
StructField("input1" -> ListType(BasicType.String)),
StructField("input2" -> ListType(BasicType.Double))
)
StructType(inputFields).get
}
override def outputSchema: StructType = StructType(StructField("output" -> TensorType.Double(outputSize))).get
}
My suggestion would be, that the apply might already work for you. I guess if you define inputSchema as follows, it might work:
override def inputSchema: StructType = {
//here you define the input
val inputFields = Seq.empty[StructField]
StructType(inputFields).get
}