[SPARK-1406] Binary classification for SVM and Logistic Regression

Author: selvinsource

mllib/src/main/scala/org/apache/spark/mllib/pmml/export/LogisticRegressionPMMLModelExport.scala renamed to mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala

@@ -21,35 +21,46 @@ import scala.{Array => SArray}
 
 import org.dmg.pmml._
 
-import org.apache.spark.mllib.classification.LogisticRegressionModel
+import org.apache.spark.mllib.regression.GeneralizedLinearModel
 
 /**
- * PMML Model Export for LogisticRegressionModel class
+ * PMML Model Export for GeneralizedLinearModel class with binary ClassificationModel
  */
-private[mllib] class LogisticRegressionPMMLModelExport(
-    model : LogisticRegressionModel, 
-    description : String) 
+private[mllib] class BinaryClassificationPMMLModelExport(
+    model : GeneralizedLinearModel, 
+    description : String,
+    normalizationMethod : RegressionNormalizationMethodType,
+    threshold: Double) 
   extends PMMLModelExport {
 
-  populateLogisticRegressionPMML(model)
+  populateBinaryClassificationPMML()
 
   /**
-   * Export the input LogisticRegressionModel model to PMML format
+   * Export the input LogisticRegressionModel or SVMModel to PMML format.
    */
-  private def populateLogisticRegressionPMML(model : LogisticRegressionModel): Unit = {
+  private def populateBinaryClassificationPMML(): Unit = {
      pmml.getHeader.setDescription(description)
 
      if (model.weights.size > 0) {
        val fields = new SArray[FieldName](model.weights.size)
        val dataDictionary = new DataDictionary
        val miningSchema = new MiningSchema
        val regressionTableYES = new RegressionTable(model.intercept).withTargetCategory("1")
-       val regressionTableNO = new RegressionTable(0.0).withTargetCategory("0")
+       var interceptNO = threshold
+       if (RegressionNormalizationMethodType.LOGIT == normalizationMethod) {
+         if (threshold <= 0)
+           interceptNO = 1000
+         else if (threshold >= 1)
+           interceptNO = -1000
+         else
+           interceptNO = -math.log(1/threshold -1)
+       }
+       val regressionTableNO = new RegressionTable(interceptNO).withTargetCategory("0")
        val regressionModel = new RegressionModel()
          .withFunctionName(MiningFunctionType.CLASSIFICATION)
          .withMiningSchema(miningSchema)
          .withModelName(description)
-         .withNormalizationMethod(RegressionNormalizationMethodType.LOGIT)
+         .withNormalizationMethod(normalizationMethod)
          .withRegressionTables(regressionTableYES, regressionTableNO)
 
        for (i <- 0 until model.weights.size) {

mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala

@@ -17,6 +17,8 @@
 
 package org.apache.spark.mllib.pmml.export
 
+import org.dmg.pmml.RegressionNormalizationMethodType
+
 import org.apache.spark.mllib.classification.LogisticRegressionModel
 import org.apache.spark.mllib.classification.SVMModel
 import org.apache.spark.mllib.clustering.KMeansModel
@@ -41,11 +43,14 @@ private[mllib] object PMMLModelExportFactory {
       case lasso: LassoModel =>
         new GeneralizedLinearPMMLModelExport(lasso, "lasso regression")
       case svm: SVMModel =>
-        new GeneralizedLinearPMMLModelExport(svm,
-          "linear SVM: if predicted value > 0, the outcome is positive, or negative otherwise")
+        new BinaryClassificationPMMLModelExport(
+          svm, "linear SVM", RegressionNormalizationMethodType.NONE, 
+          svm.getThreshold.getOrElse(0.0))
       case logistic: LogisticRegressionModel =>
-        if(logistic.numClasses == 2)
-          new LogisticRegressionPMMLModelExport(logistic, "logistic regression")
+        if (logistic.numClasses == 2)
+          new BinaryClassificationPMMLModelExport(
+            logistic, "logistic regression", RegressionNormalizationMethodType.LOGIT,
+            logistic.getThreshold.getOrElse(0.5))
         else
           throw new IllegalArgumentException(
             "PMML Export not supported for Multinomial Logistic Regression")

mllib/src/test/scala/org/apache/spark/mllib/pmml/export/LogisticRegressionPMMLModelExportSuite.scala renamed to mllib/src/test/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExportSuite.scala

@@ -18,14 +18,16 @@
 package org.apache.spark.mllib.pmml.export
 
 import org.dmg.pmml.RegressionModel
+import org.dmg.pmml.RegressionNormalizationMethodType
 import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.classification.LogisticRegressionModel
+import org.apache.spark.mllib.classification.SVMModel
 import org.apache.spark.mllib.util.LinearDataGenerator
 
-class LogisticRegressionPMMLModelExportSuite extends FunSuite {
+class BinaryClassificationPMMLModelExportSuite extends FunSuite {
 
-  test("LogisticRegressionPMMLModelExport generate PMML format") {
+  test("logistic regression PMML export") {
     val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17)
     val logisticRegressionModel =
       new LogisticRegressionModel(linearInput(0).features, linearInput(0).label)
@@ -48,5 +50,35 @@ class LogisticRegressionPMMLModelExportSuite extends FunSuite {
     // verify if there is a second table with target category 0 and no predictors
     assert(pmmlRegressionModel.getRegressionTables.get(1).getTargetCategory === "0")
     assert(pmmlRegressionModel.getRegressionTables.get(1).getNumericPredictors.size === 0)
+    // ensure logistic regression has normalization method set to LOGIT
+    assert(pmmlRegressionModel.getNormalizationMethod() == RegressionNormalizationMethodType.LOGIT)
   }
+  
+  test("linear SVM PMML export") {
+    val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17)
+    val svmModel = new SVMModel(linearInput(0).features, linearInput(0).label)
+    
+    val svmModelExport = PMMLModelExportFactory.createPMMLModelExport(svmModel)
+    
+    // assert that the PMML format is as expected
+    assert(svmModelExport.isInstanceOf[PMMLModelExport])
+    val pmml = svmModelExport.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 (for target category 1)
+    // with the same number of predictors of the model weights.
+    val pmmlRegressionModel = pmml.getModels.get(0).asInstanceOf[RegressionModel]
+    assert(pmmlRegressionModel.getRegressionTables.get(0).getTargetCategory === "1")
+    assert(pmmlRegressionModel.getRegressionTables.get(0).getNumericPredictors.size
+      === svmModel.weights.size)
+    // verify if there is a second table with target category 0 and no predictors
+    assert(pmmlRegressionModel.getRegressionTables.get(1).getTargetCategory === "0")
+    assert(pmmlRegressionModel.getRegressionTables.get(1).getNumericPredictors.size === 0)
+    // ensure linear SVM has normalization method set to NONE
+    assert(pmmlRegressionModel.getNormalizationMethod() == RegressionNormalizationMethodType.NONE)
+  }
+  
 }

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

@@ -20,13 +20,12 @@ package org.apache.spark.mllib.pmml.export
 import org.dmg.pmml.RegressionModel
 import org.scalatest.FunSuite
 
-import org.apache.spark.mllib.classification.SVMModel
 import org.apache.spark.mllib.regression.{LassoModel, LinearRegressionModel, RidgeRegressionModel}
 import org.apache.spark.mllib.util.LinearDataGenerator
 
 class GeneralizedLinearPMMLModelExportSuite extends FunSuite {
 
-  test("linear regression pmml export") {
+  test("linear regression PMML export") {
     val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17)
     val linearRegressionModel =
       new LinearRegressionModel(linearInput(0).features, linearInput(0).label)
@@ -45,7 +44,7 @@ class GeneralizedLinearPMMLModelExportSuite extends FunSuite {
       === linearRegressionModel.weights.size)
   }
 
-  test("ridge regression pmml export") {
+  test("ridge regression PMML export") {
     val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17)
     val ridgeRegressionModel =
       new RidgeRegressionModel(linearInput(0).features, linearInput(0).label)
@@ -64,7 +63,7 @@ class GeneralizedLinearPMMLModelExportSuite extends FunSuite {
       === ridgeRegressionModel.weights.size)
   }
 
-  test("lasso pmml export") {
+  test("lasso PMML export") {
     val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17)
     val lassoModel = new LassoModel(linearInput(0).features, linearInput(0).label)
     val lassoModelExport = PMMLModelExportFactory.createPMMLModelExport(lassoModel)
@@ -82,22 +81,4 @@ class GeneralizedLinearPMMLModelExportSuite extends FunSuite {
       === lassoModel.weights.size)
   }
 
-  test("svm pmml export") {
-    val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17)
-    val svmModel = new SVMModel(linearInput(0).features, linearInput(0).label)
-    val svmModelExport = PMMLModelExportFactory.createPMMLModelExport(svmModel)
-    // assert that the PMML format is as expected
-    assert(svmModelExport.isInstanceOf[PMMLModelExport])
-    val pmml = svmModelExport.getPmml
-    assert(pmml.getHeader.getDescription
-      === "linear SVM: if predicted value > 0, the outcome is positive, or negative otherwise")
-    // 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.
-    val pmmlRegressionModel = pmml.getModels.get(0).asInstanceOf[RegressionModel]
-    assert(pmmlRegressionModel.getRegressionTables.get(0).getNumericPredictors.size
-      === svmModel.weights.size)
-  }
 }

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

@@ -45,4 +45,5 @@ class KMeansPMMLModelExportSuite extends FunSuite {
     val pmmlClusteringModel = pmml.getModels.get(0).asInstanceOf[ClusteringModel]
     assert(pmmlClusteringModel.getNumberOfClusters === clusterCenters.length)
   }
+  
 }

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

@@ -40,7 +40,7 @@ class PMMLModelExportFactorySuite extends FunSuite {
   }
 
   test("PMMLModelExportFactory create GeneralizedLinearPMMLModelExport when passing a "
-    + "LinearRegressionModel, RidgeRegressionModel, LassoModel or SVMModel") {
+    + "LinearRegressionModel, RidgeRegressionModel or LassoModel") {
     val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17)
 
     val linearRegressionModel =
@@ -56,22 +56,21 @@ class PMMLModelExportFactorySuite extends FunSuite {
     val lassoModel = new LassoModel(linearInput(0).features, linearInput(0).label)
     val lassoModelExport = PMMLModelExportFactory.createPMMLModelExport(lassoModel)
     assert(lassoModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport])
-
-    val svmModel = new SVMModel(linearInput(0).features, linearInput(0).label)
-    val svmModelExport = PMMLModelExportFactory.createPMMLModelExport(svmModel)
-    assert(svmModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport])
   }
 
-  test("PMMLModelExportFactory create LogisticRegressionPMMLModelExport "
-    + "when passing a LogisticRegressionModel") {
+  test("PMMLModelExportFactory create BinaryClassificationPMMLModelExport "
+    + "when passing a LogisticRegressionModel or SVMModel") {
     val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17)
+    
     val logisticRegressionModel =
       new LogisticRegressionModel(linearInput(0).features, linearInput(0).label)
-
     val logisticRegressionModelExport =
       PMMLModelExportFactory.createPMMLModelExport(logisticRegressionModel)
-
-    assert(logisticRegressionModelExport.isInstanceOf[LogisticRegressionPMMLModelExport])
+    assert(logisticRegressionModelExport.isInstanceOf[BinaryClassificationPMMLModelExport])
+    
+    val svmModel = new SVMModel(linearInput(0).features, linearInput(0).label)
+    val svmModelExport = PMMLModelExportFactory.createPMMLModelExport(svmModel)
+    assert(svmModelExport.isInstanceOf[BinaryClassificationPMMLModelExport])
   }
 
   test("PMMLModelExportFactory throw IllegalArgumentException "