spark-instrumented-optimizer/docs/mllib-pmml-model-export.md

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global	PMML model export - MLlib	<a href="mllib-guide.html">MLlib</a> - PMML model export

• Table of contents {:toc}

MLlib supported models

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.

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`.

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.