[SPARK-7272] Initial user guide for pmml export

Author: selvinsource

docs/mllib-guide.md

@@ -39,6 +39,7 @@ filtering, dimensionality reduction, as well as underlying optimization primitiv
 * [Optimization (developer)](mllib-optimization.html)
   * stochastic gradient descent
   * limited-memory BFGS (L-BFGS)
+* [PMML model export](mllib-pmml-model-export.html)
 
 MLlib is under active development.
 The APIs marked `Experimental`/`DeveloperApi` may change in future releases, 

docs/mllib-pmml-model-export.md

@@ -9,9 +9,9 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> - PMML model export
 
 ## MLlib supported models
 
-MLlib supports model export to Predictive Model Markup Language ([PMML](http://en.wikipedia.org/wiki/Predictive_Model_Markup_Language)) format.
+MLlib supports model export to Predictive Model Markup Language ([PMML](http://en.wikipedia.org/wiki/Predictive_Model_Markup_Language)).
 
-The table below outlines the MLlib models that can be exported to PMML and their equivalent PMML format.
+The table below outlines the MLlib models that can be exported to PMML and their equivalent PMML model.
 
 <table class="table">
   <thead>
@@ -40,4 +40,47 @@ The table below outlines the MLlib models that can be exported to PMML and their
 </table>
 
 ## Examples
+<div class="codetabs">
 
+<div data-lang="scala" markdown="1">
+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 export = " + 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.
+
+</div>
+
+</div>