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MLeap runtime project, for running a Spark model on Algorithmia
package com.algorithmia
import com.algorithmia.handler.AbstractAlgorithm
import ml.combust.bundle.BundleFile
import ml.combust.bundle.dsl.Bundle
import ml.combust.mleap.core.types._
import ml.combust.mleap.runtime.MleapSupport._
import ml.combust.mleap.runtime.frame.{DefaultLeapFrame, Row, Transformer}
import scala.collection.mutable
import scala.util.{Failure, Success, Try}
class Algorithm extends AbstractAlgorithm[InputExample, String] {
//To ensure that we have a mutable type to put our mleap bundle into,
//we create a hashmap to record and store our model.
var loaded_state = new mutable.HashMap[String, Bundle[Transformer]]()
val model_uri = "data://algorithmiahq/mleap/"
// If you want to run this algorithm locally, add an Algorithmia API key into the constructor of the Algorithmia.Client below.
val client: AlgorithmiaClient = Algorithmia.client()
override def load(): Try[Unit] = {
val datafile_path = this.client.file(this.model_uri).getFile.getPath
val real_path = s"jar:file:$datafile_path"
//Now that we have the model downloaded, lets start the process of loading the bundle into the MLeap runtime.
val bundleFile: Try[Bundle[Transformer]] = BundleFile(real_path)
bundleFile match {
case Failure(exception) => return Failure(exception)
case Success(value) => this.loaded_state.put("model", value)
override def apply(input: InputExample): Try[String] = {
val schema = StructType(StructField("test_string", ScalarType.String),
StructField("test_double", ScalarType.Double)).get
val data: Seq[Row] = => Row(l.field_name, l.value))
val frame = DefaultLeapFrame(schema, data)
val bundle: Bundle[Transformer] = this.loaded_state.get("model").head
val mleapPipeline = bundle.root
val frame2 = mleapPipeline.transform(frame).get
val data2 = frame2.dataset
Success("Hello " + data2)
object Algorithm {
val handler = Algorithmia.handler(new Algorithm)
def main(args: Array[String]): Unit = {
package com.algorithmia
import play.api.libs.json._
// for JSON serialization/deserialization to work, you'll need to not only create case classes for your input and output types,
// but also companion objects with implicit reads/writes depending on if we're returning the type, or ingesting this type.
case class InputExample(rows: List[InputRow])
object InputExample{
implicit val reads: Reads[InputExample] = Json.reads[InputExample]
case class InputRow(field_name: String, value: Double)
object InputRow{
implicit val reads: Reads[InputRow] = Json.reads[InputRow]
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