adjust scala style in order to compile

Author: selvinsource

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

@@ -25,16 +25,18 @@ object ModelExportFactory {
 
   /**
    * Factory object to help creating the necessary ModelExport implementation 
-   * taking as input the ModelExportType (for example PMML) and the machine learning model (for example KMeansModel).
+   * taking as input the ModelExportType (for example PMML) 
+   * and the machine learning model (for example KMeansModel).
    */
   def createModelExport(model: Any, exportType: ModelExportType): ModelExport = {
-	 return exportType match{
-	   case PMML => model match{
-	   		case kmeans: KMeansModel => new KMeansPMMLModelExport(kmeans)
-	   		case _ => throw new IllegalArgumentException("Export not supported for model: " + model.getClass)
-	   }
-	   case _ => throw new IllegalArgumentException("Export type not supported:" + exportType)
-	 }
+    return exportType match{
+      case PMML => model match{
+        case kmeans: KMeansModel => new KMeansPMMLModelExport(kmeans)
+        case _ => 
+          throw new IllegalArgumentException("Export not supported for model: " + model.getClass)
+      }
+      case _ => throw new IllegalArgumentException("Export type not supported:" + exportType)
+      }
   }
-
+  
 }

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

@@ -19,7 +19,8 @@ package org.apache.spark.mllib.export
 
 /**
  * Defines export types.
- *  - PMML exports the machine learning models in an XML-based file format called Predictive Model Markup Language developed by the Data Mining Group (www.dmg.org).
+ *  - PMML exports the machine learning models in an XML-based file format 
+ *  called Predictive Model Markup Language developed by the Data Mining Group (www.dmg.org).
  */
 object ModelExportType extends Enumeration{
 

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

@@ -62,30 +62,42 @@ class KMeansPMMLModelExport(model : KMeansModel) extends PMMLModelExport{
        var miningSchema = new MiningSchema()
 
        for ( i <- 0 to (clusterCenter.size - 1)) {
-         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))
-       }
+         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))
+         }
 
        var comparisonMeasure = new ComparisonMeasure()
-       	.withKind(Kind.DISTANCE)
-        .withMeasure(new SquaredEuclidean()
+            .withKind(Kind.DISTANCE)
+            .withMeasure(new SquaredEuclidean()
        );
 
        dataDictionary.withNumberOfFields((dataDictionary.getDataFields()).size());
-              
+       
        pmml.setDataDictionary(dataDictionary);
 
-       var clusteringModel = new ClusteringModel(miningSchema, comparisonMeasure, MiningFunctionType.CLUSTERING, ModelClass.CENTER_BASED, model.clusterCenters.length)
-       	.withModelName("k-means");
+       var clusteringModel = new ClusteringModel(miningSchema, comparisonMeasure, 
+        MiningFunctionType.CLUSTERING, ModelClass.CENTER_BASED, model.clusterCenters.length)
+        .withModelName("k-means");
 
        for ( i <- 0 to (clusterCenter.size - 1)) {
-    	   clusteringModel.withClusteringFields(new ClusteringField(fields(i)).withCompareFunction(CompareFunctionType.ABS_DIFF))
-    	   var cluster = new Cluster().withName("cluster_"+i).withArray(new org.dmg.pmml.Array().withType(Type.REAL).withN(clusterCenter.size).withValue(model.clusterCenters(i).toArray.mkString(" ")))
-    	   //cluster.withSize(value) //we don't have the size of the single cluster but only the centroids (withValue)
-    	   clusteringModel.withClusters(cluster)
+         clusteringModel.withClusteringFields(
+             new ClusteringField(fields(i)).withCompareFunction(CompareFunctionType.ABS_DIFF)
+         )
+         var cluster = new Cluster()
+            .withName("cluster_" + i)
+            .withArray(new org.dmg.pmml.Array()
+            .withType(Type.REAL)
+            .withN(clusterCenter.size)
+            .withValue(model.clusterCenters(i).toArray.mkString(" ")))
+            //we don't have the size of the single cluster but only the centroids (withValue)
+            //.withSize(value)
+         clusteringModel.withClusters(cluster)
        }
-
+       
        pmml.withModels(clusteringModel);
 
      }

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

@@ -41,13 +41,14 @@ trait PMMLModelExport extends ModelExport{
 
   private def setHeader(pmml : PMML): Unit = {
     var version = getClass().getPackage().getImplementationVersion()
-	var app = new Application().withName("Apache Spark MLlib").withVersion(version)
-	var timestamp = new Timestamp().withContent(new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss").format(new Date()))
-	var header = new Header()
-    	.withCopyright("www.dmg.org")
+    var app = new Application().withName("Apache Spark MLlib").withVersion(version)
+    var timestamp = new Timestamp()
+        .withContent(new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss").format(new Date()))
+    var header = new Header()
+        .withCopyright("www.dmg.org")
         .withApplication(app)
         .withTimestamp(timestamp);
-	pmml.setHeader(header);
+    pmml.setHeader(header);
   } 
 
   /**