[SPARK-1406] added linear SVM PMML export

Author: selvinsource

mllib/src/main/scala/org/apache/spark/mllib/export/ModelExportFactory.scala

@@ -17,6 +17,7 @@
 
 package org.apache.spark.mllib.export
 
+import org.apache.spark.mllib.classification.SVMModel
 import org.apache.spark.mllib.clustering.KMeansModel
 import org.apache.spark.mllib.export.ModelExportType.ModelExportType
 import org.apache.spark.mllib.export.ModelExportType.PMML
@@ -44,6 +45,8 @@ private[mllib] object ModelExportFactory {
           new GeneralizedLinearPMMLModelExport(ridgeRegression, "ridge regression")
         case lassoRegression: LassoModel => 
           new GeneralizedLinearPMMLModelExport(lassoRegression, "lasso regression")
+        case svm: SVMModel => 
+          new GeneralizedLinearPMMLModelExport(svm, "linear SVM")
         case _ => 
           throw new IllegalArgumentException("Export not supported for model: " + model.getClass)
       }

mllib/src/test/scala/org/apache/spark/mllib/export/ModelExportFactorySuite.scala

@@ -19,6 +19,7 @@ package org.apache.spark.mllib.export
 
 import org.scalatest.FunSuite
 
+import org.apache.spark.mllib.classification.SVMModel
 import org.apache.spark.mllib.clustering.KMeansModel
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LassoModel
@@ -48,15 +49,16 @@ class ModelExportFactorySuite extends FunSuite{
 
    }
 
-   test("ModelExportFactory create GeneralizedLinearPMMLModelExport when passing a"
-       +"LinearRegressionModel, RidgeRegressionModel or LassoModel") {
+   test("ModelExportFactory create GeneralizedLinearPMMLModelExport when passing a "
+       +"LinearRegressionModel, RidgeRegressionModel, LassoModel or SVMModel") {
 
     //arrange
     val linearInput = LinearDataGenerator.generateLinearInput(
       3.0, Array(10.0, 10.0), 1, 17)
     val linearRegressionModel = new LinearRegressionModel(linearInput(0).features, linearInput(0).label);
     val ridgeRegressionModel = new RidgeRegressionModel(linearInput(0).features, linearInput(0).label);
     val lassoModel = new LassoModel(linearInput(0).features, linearInput(0).label);
+    val svmModel = new SVMModel(linearInput(0).features, linearInput(0).label);
 
     //act
     val linearModelExport = ModelExportFactory.createModelExport(linearRegressionModel, ModelExportType.PMML)         
@@ -73,6 +75,11 @@ class ModelExportFactorySuite extends FunSuite{
     //assert
     assert(lassoModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport])
 
+    //act
+    val svmModelExport = ModelExportFactory.createModelExport(svmModel, ModelExportType.PMML)         
+    //assert
+    assert(svmModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport])
+    
    }
 
    test("ModelExportFactory throw IllegalArgumentException when passing an unsupported model") {

mllib/src/test/scala/org/apache/spark/mllib/export/pmml/GeneralizedLinearPMMLModelExportSuite.scala

@@ -20,6 +20,7 @@ package org.apache.spark.mllib.export.pmml
 import org.dmg.pmml.RegressionModel
 import org.scalatest.FunSuite
 
+import org.apache.spark.mllib.classification.SVMModel
 import org.apache.spark.mllib.export.ModelExportFactory
 import org.apache.spark.mllib.export.ModelExportType
 import org.apache.spark.mllib.regression.LassoModel
@@ -37,6 +38,7 @@ class GeneralizedLinearPMMLModelExportSuite extends FunSuite{
     val linearRegressionModel = new LinearRegressionModel(linearInput(0).features, linearInput(0).label);
     val ridgeRegressionModel = new RidgeRegressionModel(linearInput(0).features, linearInput(0).label);
     val lassoModel = new LassoModel(linearInput(0).features, linearInput(0).label);
+    val svmModel = new SVMModel(linearInput(0).features, linearInput(0).label);
 
     //act by exporting the model to the PMML format
     val linearModelExport = ModelExportFactory.createModelExport(linearRegressionModel, ModelExportType.PMML)         
@@ -76,11 +78,25 @@ class GeneralizedLinearPMMLModelExportSuite extends FunSuite{
     //it also verifies that the pmml model has a regression table with the same number of predictors of the model weights
     assert(pmml.getModels().get(0).asInstanceOf[RegressionModel]
      .getRegressionTables().get(0).getNumericPredictors().size() === lassoModel.weights.size)
+     
+    //act
+    val svmModelExport = ModelExportFactory.createModelExport(svmModel, ModelExportType.PMML)         
+    //assert that the PMML format is as expected
+    assert(svmModelExport.isInstanceOf[PMMLModelExport])
+    pmml = svmModelExport.asInstanceOf[PMMLModelExport].getPmml()
+    assert(pmml.getHeader().getDescription() === "linear SVM")
+    //check that the number of fields match the weights size
+    assert(pmml.getDataDictionary().getNumberOfFields() === svmModel.weights.size + 1)
+    //this verify that there is a model attached to the pmml object and the model is a regression one
+    //it also verifies that the pmml model has a regression table with the same number of predictors of the model weights
+    assert(pmml.getModels().get(0).asInstanceOf[RegressionModel]
+     .getRegressionTables().get(0).getNumericPredictors().size() === svmModel.weights.size)
 
     //manual checking
     //ModelExporter.toPMML(linearRegressionModel,"/tmp/linearregression.xml")
     //ModelExporter.toPMML(ridgeRegressionModel,"/tmp/ridgeregression.xml")
     //ModelExporter.toPMML(lassoModel,"/tmp/lassoregression.xml")
+    //ModelExporter.toPMML(svmModel,"/tmp/svm.xml")
 
    }