[SPARK-7979] Enforce structural type checker.

Author: rxin

core/src/test/scala/org/apache/spark/util/random/XORShiftRandomSuite.scala

@@ -28,7 +28,7 @@ import scala.language.reflectiveCalls
 
 class XORShiftRandomSuite extends SparkFunSuite with Matchers {
 
-  def fixture: Object {val seed: Long; val hundMil: Int; val xorRand: XORShiftRandom} = new {
+  private def fixture = new {
     val seed = 1L
     val xorRand = new XORShiftRandom(seed)
     val hundMil = 1e8.toInt

examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala

@@ -22,13 +22,12 @@ import scala.language.reflectiveCalls
 import scopt.OptionParser
 
 import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.evaluation.MulticlassMetrics
-import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{DecisionTree, RandomForest, impurity}
 import org.apache.spark.mllib.tree.configuration.{Algo, Strategy}
 import org.apache.spark.mllib.tree.configuration.Algo._
+import org.apache.spark.mllib.tree.model.DecisionTreeModel
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.Utils
@@ -354,7 +353,11 @@ object DecisionTreeRunner {
 
   /**
    * Calculates the mean squared error for regression.
+   *
+   * This is just for demo purpose. In general, don't copy this code because it is no very efficient
+   * due to the use of structural types, which leads to one reflection call per record.
    */
+  // scalastyle:off structural.type
   private[mllib] def meanSquaredError(
       model: { def predict(features: Vector): Double },
       data: RDD[LabeledPoint]): Double = {
@@ -363,4 +366,5 @@ object DecisionTreeRunner {
       err * err
     }.mean()
   }
+  // scalastyle:on structural.type
 }

graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala

@@ -41,14 +41,16 @@ abstract class EdgeRDD[ED](
     @transient sc: SparkContext,
     @transient deps: Seq[Dependency[_]]) extends RDD[Edge[ED]](sc, deps) {
 
+  // scalastyle:off structural.type
   private[graphx] def partitionsRDD: RDD[(PartitionID, EdgePartition[ED, VD])] forSome { type VD }
+  // scalastyle:on structural.type
 
   override protected def getPartitions: Array[Partition] = partitionsRDD.partitions
 
   override def compute(part: Partition, context: TaskContext): Iterator[Edge[ED]] = {
     val p = firstParent[(PartitionID, EdgePartition[ED, _])].iterator(part, context)
     if (p.hasNext) {
-      p.next._2.iterator.map(_.copy())
+      p.next()._2.iterator.map(_.copy())
     } else {
       Iterator.empty
     }

mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala

@@ -37,11 +37,13 @@ import org.apache.spark.storage.StorageLevel
  */
 private[ml] trait OneVsRestParams extends PredictorParams {
 
+  // scalastyle:off structural.type
   type ClassifierType = Classifier[F, E, M] forSome {
     type F
     type M <: ClassificationModel[F, M]
     type E <: Classifier[F, E, M]
   }
+  // scalastyle:on structural.type
 
   /**
    * param for the base binary classifier that we reduce multiclass classification into.

scalastyle-config.xml

@@ -114,6 +114,9 @@
  <!--   <parameter name="maximum"><![CDATA[10]]></parameter> -->
  <!--  </parameters> -->
  <!-- </check> -->
+
+  <check level="error" class="org.scalastyle.scalariform.StructuralTypeChecker" enabled="true"></check>
+
  <check level="error" class="org.scalastyle.scalariform.UppercaseLChecker" enabled="true"></check>
  <check level="error" class="org.scalastyle.scalariform.SimplifyBooleanExpressionChecker" enabled="false"></check>
  <check level="error" class="org.scalastyle.scalariform.IfBraceChecker" enabled="true">