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 model | PMML model |
|---|---|
| KMeansModel | ClusteringModel |
| LinearRegressionModel | RegressionModel (functionName="regression") |
| RidgeRegressionModel | RegressionModel (functionName="regression") |
| LassoModel | RegressionModel (functionName="regression") |
| SVMModel | RegressionModel (functionName="classification" normalizationMethod="none") |
| Binary LogisticRegressionModel | RegressionModel (functionName="classification" normalizationMethod="logit") |
Examples
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 = 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 to a String in PMML format println(s“PMML Model:\n ${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)
For unsupported models, either you will not find a .toPMML method or an IllegalArgumentException will be thrown.