more code style

tdas · Apr 21, 2015 · 3c22f79 · 3c22f79
1 parent e2313df
commit 3c22f79
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Showing 7 changed files with 73 additions and 83 deletions.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala
@@ -17,9 +17,7 @@
 
 package org.apache.spark.mllib.pmml
 
-import java.io.File
-import java.io.OutputStream
-import java.io.StringWriter
+import java.io.{File, OutputStream, StringWriter}
 import javax.xml.transform.stream.StreamResult
 
 import org.jpmml.model.JAXBUtil
@@ -33,37 +31,37 @@ import org.apache.spark.mllib.pmml.export.PMMLModelExportFactory
  * developed by the Data Mining Group (www.dmg.org).
  */
 trait PMMLExportable {
-  
+
   /**
    * Export the model to the stream result in PMML format
    */
   private def toPMML(streamResult: StreamResult): Unit = {
     val pmmlModelExport = PMMLModelExportFactory.createPMMLModelExport(this)
     JAXBUtil.marshalPMML(pmmlModelExport.getPmml, streamResult)
   }
-  
+
   /**
    * Export the model to a local file in PMML format
    */
   def toPMML(localPath: String): Unit = {
     toPMML(new StreamResult(new File(localPath)))
   }
-  
+
   /**
    * Export the model to a directory on a distributed file system in PMML format
    */
   def toPMML(sc: SparkContext, path: String): Unit = {
     val pmml = toPMML()
     sc.parallelize(Array(pmml), 1).saveAsTextFile(path)
   }
-  
+
   /**
    * Export the model to the OutputStream in PMML format
    */
   def toPMML(outputStream: OutputStream): Unit = {
     toPMML(new StreamResult(outputStream))
   }
-  
+
   /**
    * Export the model to a String in PMML format
    */
@@ -72,5 +70,5 @@ trait PMMLExportable {
     toPMML(new StreamResult(writer))
     writer.toString
   }
-  
+
 }
diff --git a/.../src/main/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExport.scala b/.../src/main/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExport.scala
@@ -27,47 +27,47 @@ import org.apache.spark.mllib.regression.GeneralizedLinearModel
  * PMML Model Export for GeneralizedLinearModel abstract class
  */
 private[mllib] class GeneralizedLinearPMMLModelExport(
-    model : GeneralizedLinearModel, 
-    description : String) 
-  extends PMMLModelExport{
+    model: GeneralizedLinearModel,
+    description: String)
+  extends PMMLModelExport {
 
   populateGeneralizedLinearPMML(model)
 
   /**
    * Export the input GeneralizedLinearModel model to PMML format.
    */
   private def populateGeneralizedLinearPMML(model: GeneralizedLinearModel): Unit = {
-     pmml.getHeader.setDescription(description)
+    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 regressionTable = new RegressionTable(model.intercept)
-       val regressionModel = new RegressionModel(miningSchema,MiningFunctionType.REGRESSION)
-         .withModelName(description)
-         .withRegressionTables(regressionTable)
+    if (model.weights.size > 0) {
+      val fields = new SArray[FieldName](model.weights.size)
+      val dataDictionary = new DataDictionary
+      val miningSchema = new MiningSchema
+      val regressionTable = new RegressionTable(model.intercept)
+      val regressionModel = new RegressionModel(miningSchema, MiningFunctionType.REGRESSION)
+        .withModelName(description)
+        .withRegressionTables(regressionTable)
 
-       for (i <- 0 until model.weights.size) {
-         fields(i) = FieldName.create("field_" + i)
-         dataDictionary.withDataFields(new DataField(fields(i), OpType.CONTINUOUS, DataType.DOUBLE))
-         miningSchema
-           .withMiningFields(new MiningField(fields(i))
-           .withUsageType(FieldUsageType.ACTIVE))
-         regressionTable.withNumericPredictors(new NumericPredictor(fields(i), model.weights(i)))   
-       }
-
-       // for completeness add target field
-       val targetField = FieldName.create("target")
-       dataDictionary.withDataFields(new DataField(targetField, OpType.CONTINUOUS, DataType.DOUBLE))
-       miningSchema
-         .withMiningFields(new MiningField(targetField)
-         .withUsageType(FieldUsageType.TARGET))
+      for (i <- 0 until model.weights.size) {
+        fields(i) = FieldName.create("field_" + i)
+        dataDictionary.withDataFields(new DataField(fields(i), OpType.CONTINUOUS, DataType.DOUBLE))
+        miningSchema
+          .withMiningFields(new MiningField(fields(i))
+          .withUsageType(FieldUsageType.ACTIVE))
+        regressionTable.withNumericPredictors(new NumericPredictor(fields(i), model.weights(i)))
+      }
 
-       dataDictionary.withNumberOfFields(dataDictionary.getDataFields.size)
+      // for completeness add target field
+      val targetField = FieldName.create("target")
+      dataDictionary.withDataFields(new DataField(targetField, OpType.CONTINUOUS, DataType.DOUBLE))
+      miningSchema
+        .withMiningFields(new MiningField(targetField)
+        .withUsageType(FieldUsageType.TARGET))
 
-       pmml.setDataDictionary(dataDictionary)
-       pmml.withModels(regressionModel)
-     }
+      dataDictionary.withNumberOfFields(dataDictionary.getDataFields.size)
+
+      pmml.setDataDictionary(dataDictionary)
+      pmml.withModels(regressionModel)
+    }
   }
 }
diff --git a/...src/main/scala/org/apache/spark/mllib/pmml/export/LogisticRegressionPMMLModelExport.scala b/...src/main/scala/org/apache/spark/mllib/pmml/export/LogisticRegressionPMMLModelExport.scala
@@ -29,7 +29,7 @@ import org.apache.spark.mllib.classification.LogisticRegressionModel
 private[mllib] class LogisticRegressionPMMLModelExport(
     model : LogisticRegressionModel, 
     description : String) 
-  extends PMMLModelExport{
+  extends PMMLModelExport {
 
   populateLogisticRegressionPMML(model)
 

diff --git a/...test/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExportSuite.scala b/...test/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExportSuite.scala
@@ -21,9 +21,7 @@ import org.dmg.pmml.RegressionModel
 import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.classification.SVMModel
-import org.apache.spark.mllib.regression.LassoModel
-import org.apache.spark.mllib.regression.LinearRegressionModel
-import org.apache.spark.mllib.regression.RidgeRegressionModel
+import org.apache.spark.mllib.regression.{LassoModel, LinearRegressionModel, RidgeRegressionModel}
 import org.apache.spark.mllib.util.LinearDataGenerator
 
 class GeneralizedLinearPMMLModelExportSuite extends FunSuite {
@@ -87,7 +85,7 @@ class GeneralizedLinearPMMLModelExportSuite extends FunSuite {
   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)         
+    val svmModelExport = PMMLModelExportFactory.createPMMLModelExport(svmModel)
     // assert that the PMML format is as expected
     assert(svmModelExport.isInstanceOf[PMMLModelExport])
     val pmml = svmModelExport.getPmml

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExportSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExportSuite.scala
@@ -26,15 +26,14 @@ import org.apache.spark.mllib.linalg.Vectors
 class KMeansPMMLModelExportSuite extends FunSuite {
 
   test("KMeansPMMLModelExport generate PMML format") {
-    // arrange model to test
     val clusterCenters = Array(
       Vectors.dense(1.0, 2.0, 6.0),
       Vectors.dense(1.0, 3.0, 0.0),
       Vectors.dense(1.0, 4.0, 6.0))
     val kmeansModel = new KMeansModel(clusterCenters)
-    
+
     val modelExport = PMMLModelExportFactory.createPMMLModelExport(kmeansModel)
-    
+
     // assert that the PMML format is as expected
     assert(modelExport.isInstanceOf[PMMLModelExport])
     val pmml = modelExport.asInstanceOf[PMMLModelExport].getPmml

diff --git a/...est/scala/org/apache/spark/mllib/pmml/export/LogisticRegressionPMMLModelExportSuite.scala b/...est/scala/org/apache/spark/mllib/pmml/export/LogisticRegressionPMMLModelExportSuite.scala
@@ -23,13 +23,13 @@ import org.scalatest.FunSuite
 import org.apache.spark.mllib.classification.LogisticRegressionModel
 import org.apache.spark.mllib.util.LinearDataGenerator
 
-class LogisticRegressionPMMLModelExportSuite extends FunSuite{
+class LogisticRegressionPMMLModelExportSuite extends FunSuite {
 
   test("LogisticRegressionPMMLModelExport generate PMML format") {
     val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17)
     val logisticRegressionModel =
       new LogisticRegressionModel(linearInput(0).features, linearInput(0).label)
-    
+
     val logisticModelExport = PMMLModelExportFactory.createPMMLModelExport(logisticRegressionModel)
 
     // assert that the PMML format is as expected
@@ -48,5 +48,5 @@ 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)
-   }
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactorySuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactorySuite.scala
@@ -19,13 +19,10 @@ package org.apache.spark.mllib.pmml.export
 
 import org.scalatest.FunSuite
 
-import org.apache.spark.mllib.classification.LogisticRegressionModel
-import org.apache.spark.mllib.classification.SVMModel
+import org.apache.spark.mllib.classification.{LogisticRegressionModel, SVMModel}
 import org.apache.spark.mllib.clustering.KMeansModel
 import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.regression.LassoModel
-import org.apache.spark.mllib.regression.LinearRegressionModel
-import org.apache.spark.mllib.regression.RidgeRegressionModel
+import org.apache.spark.mllib.regression.{LassoModel, LinearRegressionModel, RidgeRegressionModel}
 import org.apache.spark.mllib.util.LinearDataGenerator
 
 class PMMLModelExportFactorySuite extends FunSuite {
@@ -38,33 +35,32 @@ class PMMLModelExportFactorySuite extends FunSuite {
     val kmeansModel = new KMeansModel(clusterCenters)
 
     val modelExport = PMMLModelExportFactory.createPMMLModelExport(kmeansModel)
-         
+
     assert(modelExport.isInstanceOf[KMeansPMMLModelExport])
-   }
-
-   test("PMMLModelExportFactory create GeneralizedLinearPMMLModelExport when passing a "
-       + "LinearRegressionModel, RidgeRegressionModel, LassoModel or SVMModel") {
-     val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17)
+  }
 
-     val linearRegressionModel =
-       new LinearRegressionModel(linearInput(0).features, linearInput(0).label)
-     val linearModelExport = PMMLModelExportFactory.createPMMLModelExport(linearRegressionModel)
-     assert(linearModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport])
+  test("PMMLModelExportFactory create GeneralizedLinearPMMLModelExport when passing a "
+    + "LinearRegressionModel, RidgeRegressionModel, LassoModel or SVMModel") {
+    val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17)
 
-     val ridgeRegressionModel =
-       new RidgeRegressionModel(linearInput(0).features, linearInput(0).label)
-     val ridgeModelExport = PMMLModelExportFactory.createPMMLModelExport(ridgeRegressionModel)
-     assert(ridgeModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport])
+    val linearRegressionModel =
+      new LinearRegressionModel(linearInput(0).features, linearInput(0).label)
+    val linearModelExport = PMMLModelExportFactory.createPMMLModelExport(linearRegressionModel)
+    assert(linearModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport])
 
+    val ridgeRegressionModel =
+      new RidgeRegressionModel(linearInput(0).features, linearInput(0).label)
+    val ridgeModelExport = PMMLModelExportFactory.createPMMLModelExport(ridgeRegressionModel)
+    assert(ridgeModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport])
 
-     val lassoModel = new LassoModel(linearInput(0).features, linearInput(0).label)
-     val lassoModelExport = PMMLModelExportFactory.createPMMLModelExport(lassoModel)
-     assert(lassoModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport])
+    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])
-   }
+    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") {
@@ -76,14 +72,13 @@ class PMMLModelExportFactorySuite extends FunSuite {
       PMMLModelExportFactory.createPMMLModelExport(logisticRegressionModel)
 
     assert(logisticRegressionModelExport.isInstanceOf[LogisticRegressionPMMLModelExport])
-   }
-
-   test("PMMLModelExportFactory throw IllegalArgumentException "
-       + "when passing an unsupported model") {
+  }
+
+  test("PMMLModelExportFactory throw IllegalArgumentException when passing an unsupported model") {
     val invalidModel = new Object
-    
+
     intercept[IllegalArgumentException] {
       PMMLModelExportFactory.createPMMLModelExport(invalidModel)
     }
-   }
+  }
 }