PMML model export - RDD-based API

spark.mllib supported models

spark.mllib supports model export to Predictive Model Markup Language (PMML).

The table below outlines the spark.mllib models that can be exported to PMML and their equivalent PMML model.

spark.mllib modelPMML model
LinearRegressionModelRegressionModel (functionName="regression")
RidgeRegressionModelRegressionModel (functionName="regression")
LassoModelRegressionModel (functionName="regression")
SVMModelRegressionModel (functionName="classification" normalizationMethod="none")
Binary LogisticRegressionModelRegressionModel (functionName="classification" normalizationMethod="logit")


To export a supported model (see table above) to PMML, simply call model.toPMML.

As well as exporting the PMML model to a String (model.toPMML as in the example above), you can export the PMML model to other formats.

Refer to the KMeans Scala docs and Vectors Scala docs for details on the API.

Here a complete example of building a KMeansModel and print it out in PMML format:

import org.apache.spark.mllib.clustering.KMeans
import org.apache.spark.mllib.linalg.Vectors

// Load and parse the data
val data = sc.textFile("data/mllib/kmeans_data.txt")
val parsedData = => Vectors.dense(s.split(' ').map(_.toDouble))).cache()

// Cluster the data into two classes using KMeans
val numClusters = 2
val numIterations = 20
val clusters = KMeans.train(parsedData, numClusters, numIterations)

// Export to PMML to a String in PMML format
println(s"PMML Model:\n ${clusters.toPMML}")

// Export the model to a local file in PMML format

// Export the model to a directory on a distributed file system in PMML format
clusters.toPMML(sc, "/tmp/kmeans")

// Export the model to the OutputStream in PMML format
Find full example code at "examples/src/main/scala/org/apache/spark/examples/mllib/PMMLModelExportExample.scala" in the Spark repo.

For unsupported models, either you will not find a .toPMML method or an IllegalArgumentException will be thrown.