An example of using Scala to call the predict function from a Scikit-Learn RandomForestRegressor

randomforestregressor_predict.scala

import rapture.json.jsonBackends.jawn._
import rapture.json.Json
import scala.annotation.tailrec


case class RandomForestTree(
  treeId: Int,
  undefinedIndex: Int,
  features: Array[Int],
  thresholds: Array[Double],
  childrenLeft: Array[Int],
  childrenRight: Array[Int],
  values: Array[Double]
) extends Serializable {
  def walkTree(inputs: Array[Double]): Double = {
    @tailrec
    def subwalk(node: Int): Int = {
      if (features(node) == undefinedIndex) {
        node
      } else {
        val featureIndex = features(node)
        val threshold = thresholds(node)
        val inputFeatureValue = inputs(featureIndex)
        if (inputFeatureValue <= threshold) {
          subwalk(childrenLeft(node))
        } else {
          subwalk(childrenRight(node))
        }
      }
    }

    val finalNode = subwalk(0)
    values(finalNode)
  }
}

case class RandomForestRegressor(name: String, trees: Array[RandomForestTree]) {

  def transform(input: Array[Double]): Double = {

    val treeResults = trees.map(_.walkTree(input))
    val score = treeResults.sum / trees.length

    score
  }
}

object RandomForestTree {
  def fromJSON(json: Json): RandomForestTree = {
    RandomForestTree(
      json.i.as[Int],
      json.tree_undefined.as[Int],
      json.features.as[Array[Int]],
      json.thresholds.as[Array[Double]],
      json.children_left.as[Array[Int]],
      json.children_right.as[Array[Int]],
      json.values.as[Array[Double]]
    )
  }
}


object RandomForestRegressor {

  def fromJSON(json: Json): RandomForestRegressor = {
    import rapture.json.jsonBackends.jawn._
    RandomForestRegressor(
      name = json.name.as[String],
      trees = json.trees.as[Array[Json]].map(RandomForestTree.fromJSON)
    )
  }
}