MLlib supports model export to Predictive Model Markup Language (PMML).
The table below outlines the MLlib models that can be exported to PMML and their equivalent PMML model.
Here a complete example of building a KMeansModel and print it out in PMML format: {% highlight scala %} 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 = data.map(s => 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 println(“PMML Model:\n” + clusters.toPMML) {% endhighlight %}
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:
{% highlight scala %} // Export the model to a String in PMML format clusters.toPMML
// Export the model to a local file in PMML format clusters.toPMML(“/tmp/kmeans.xml”)
// 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 clusters.toPMML(System.out) {% endhighlight %}
For unsupported models, either you will not find a .toPMML method or an IllegalArgumentException will be thrown.