another pass on code style

mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala

@@ -34,14 +34,13 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
  *                                1, 2, ... , k-1. It's important to note that features are
  *                                zero-indexed.
  */
-class Strategy  (
-                val algo: Algo,
-                val impurity: Impurity,
-                val maxDepth: Int,
-                val maxBins: Int = 100,
-                val quantileCalculationStrategy: QuantileStrategy = Sort,
-                val categoricalFeaturesInfo: Map[Int,Int] = Map[Int,Int]()) extends Serializable {
+class Strategy (
+    val algo: Algo,
+    val impurity: Impurity,
+    val maxDepth: Int,
+    val maxBins: Int = 100,
+    val quantileCalculationStrategy: QuantileStrategy = Sort,
+    val categoricalFeaturesInfo: Map[Int,Int] = Map[Int,Int]()) extends Serializable {
 
   var numBins: Int  = Int.MinValue
-
 }

mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala

@@ -17,8 +17,6 @@
 
 package org.apache.spark.mllib.tree.impurity
 
-import java.lang.UnsupportedOperationException
-
 /**
  * Class for calculating [[http://en.wikipedia.org/wiki/Binary_entropy_function entropy]] during
  * binary classification.

mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala

@@ -17,32 +17,30 @@
 
 package org.apache.spark.mllib.tree.impurity
 
-import java.lang.UnsupportedOperationException
-
 /**
- * Class for calculating the [[http://en.wikipedia
- * .org/wiki/Decision_tree_learning#Gini_impurity]] during binary classification
+ * Class for calculating the
+ * [[http://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity Gini impurity]]
+ * during binary classification.
  */
 object Gini extends Impurity {
 
   /**
-   * gini coefficient calculation
+   * Gini coefficient calculation
    * @param c0 count of instances with label 0
    * @param c1 count of instances with label 1
-   * @return gini coefficient value
+   * @return Gini coefficient value
    */
-  def calculate(c0 : Double, c1 : Double): Double = {
+  override def calculate(c0: Double, c1: Double): Double = {
     if (c0 == 0 || c1 == 0) {
       0
     } else {
       val total = c0 + c1
       val f0 = c0 / total
       val f1 = c1 / total
-      1 - f0*f0 - f1*f1
+      1 - f0 * f0 - f1 * f1
     }
   }
 
   def calculate(count: Double, sum: Double, sumSquares: Double): Double =
     throw new UnsupportedOperationException("Gini.calculate")
-
 }

mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala

@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.tree.impurity
 
 /**
- * Trail for calculating information gain
+ * Trait for calculating information gain.
  */
 trait Impurity extends Serializable {
 

mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala

@@ -17,25 +17,21 @@
 
 package org.apache.spark.mllib.tree.impurity
 
-import java.lang.UnsupportedOperationException
-
 /**
  * Class for calculating variance during regression
  */
 object Variance extends Impurity {
-   def calculate(c0: Double, c1: Double): Double
-   = throw new UnsupportedOperationException("Variance.calculate")
+   override def calculate(c0: Double, c1: Double): Double =
+     throw new UnsupportedOperationException("Variance.calculate")
 
   /**
    * variance calculation
    * @param count number of instances
    * @param sum sum of labels
    * @param sumSquares summation of squares of the labels
-   * @return
    */
-  def calculate(count: Double, sum: Double, sumSquares: Double): Double = {
-    val squaredLoss = sumSquares - (sum*sum)/count
-    squaredLoss/count
+  override def calculate(count: Double, sum: Double, sumSquares: Double): Double = {
+    val squaredLoss = sumSquares - (sum * sum) / count
+    squaredLoss / count
   }
-
 }

mllib/src/main/scala/org/apache/spark/mllib/tree/model/Bin.scala

@@ -30,6 +30,4 @@ import org.apache.spark.mllib.tree.configuration.FeatureType._
  * @param featureType type of feature -- categorical or continuous
  * @param category categorical label value accepted in the bin
  */
-case class Bin(lowSplit: Split, highSplit: Split, featureType: FeatureType, category: Double) {
-
-}
+case class Bin(lowSplit: Split, highSplit: Split, featureType: FeatureType, category: Double)

mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala

@@ -46,6 +46,4 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
   def predict(features: RDD[Array[Double]]): RDD[Double] = {
     features.map(x => predict(x))
   }
-
-
 }

mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala

@@ -36,6 +36,4 @@ class InformationGainStats(
     "gain = %f, impurity = %f, left impurity = %f, right impurity = %f, predict = %f"
       .format(gain, impurity, leftImpurity, rightImpurity, predict)
   }
-
-
 }

mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala

@@ -37,7 +37,7 @@ class Node (
     val split: Option[Split],
     var leftNode: Option[Node],
     var rightNode: Option[Node],
-    val stats: Option[InformationGainStats]) extends Serializable with Logging{
+    val stats: Option[InformationGainStats]) extends Serializable with Logging {
 
   override def toString = "id = " + id + ", isLeaf = " + isLeaf + ", predict = " + predict + ", " +
     "split = " + split + ", stats = " + stats
@@ -46,7 +46,7 @@ class Node (
    * build the left node and right nodes if not leaf
    * @param nodes array of nodes
    */
-  def build(nodes : Array[Node]): Unit = {
+  def build(nodes: Array[Node]): Unit = {
 
     logDebug("building node " + id + " at level " +
       (scala.math.log(id + 1)/scala.math.log(2)).toInt )
@@ -68,7 +68,7 @@ class Node (
    * @param feature feature value
    * @return predicted value
    */
-  def predictIfLeaf(feature : Array[Double]) : Double = {
+  def predictIfLeaf(feature: Array[Double]) : Double = {
     if (isLeaf) {
       predict
     } else{
@@ -87,5 +87,4 @@ class Node (
       }
     }
   }
-
 }

mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala

@@ -42,15 +42,15 @@ case class Split(
  * @param feature feature index
  * @param featureType type of feature -- categorical or continuous
  */
-class DummyLowSplit(feature: Int, featureType : FeatureType)
+class DummyLowSplit(feature: Int, featureType: FeatureType)
   extends Split(feature, Double.MinValue, featureType, List())
 
 /**
  * Split with maximum threshold for continuous features. Helps with the highest bin creation.
  * @param feature feature index
  * @param featureType type of feature -- categorical or continuous
  */
-class DummyHighSplit(feature: Int, featureType : FeatureType)
+class DummyHighSplit(feature: Int, featureType: FeatureType)
   extends Split(feature, Double.MaxValue, featureType, List())
 
 /**
@@ -59,6 +59,6 @@ class DummyHighSplit(feature: Int, featureType : FeatureType)
  * @param feature feature index
  * @param featureType type of feature -- categorical or continuous
  */
-class DummyCategoricalSplit(feature: Int, featureType : FeatureType)
+class DummyCategoricalSplit(feature: Int, featureType: FeatureType)
   extends Split(feature, Double.MaxValue, featureType, List())