minor improvements to docs and style

Author: manishamde

mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala

@@ -17,7 +17,6 @@
 
 package org.apache.spark.mllib.tree
 
-import scala.util.control.Breaks._
 import org.apache.spark.SparkContext._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.tree.model._
@@ -29,6 +28,7 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
 import org.apache.spark.mllib.tree.configuration.FeatureType._
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.impurity.{Variance, Entropy, Gini, Impurity}
+import scala.util.control.Breaks._
 
 /**
  * A class that implements a decision tree algorithm for classification and regression. It
@@ -181,8 +181,8 @@ object DecisionTree extends Serializable with Logging {
       input: RDD[LabeledPoint],
       algo: Algo,
       impurity: Impurity,
-      maxDepth: Int
-      ): DecisionTreeModel = {
+      maxDepth: Int)
+    : DecisionTreeModel = {
     val strategy = new Strategy(algo,impurity,maxDepth)
     new DecisionTree(strategy).train(input: RDD[LabeledPoint])
   }
@@ -211,8 +211,8 @@ object DecisionTree extends Serializable with Logging {
       maxDepth: Int,
       maxBins: Int,
       quantileCalculationStrategy: QuantileStrategy,
-      categoricalFeaturesInfo: Map[Int,Int]
-      ): DecisionTreeModel = {
+      categoricalFeaturesInfo: Map[Int,Int])
+    : DecisionTreeModel = {
     val strategy = new Strategy(algo, impurity, maxDepth, maxBins, quantileCalculationStrategy,
       categoricalFeaturesInfo)
     new DecisionTree(strategy).train(input: RDD[LabeledPoint])
@@ -238,7 +238,8 @@ object DecisionTree extends Serializable with Logging {
       level: Int,
       filters: Array[List[Filter]],
       splits: Array[Array[Split]],
-      bins: Array[Array[Bin]]): Array[(Split, InformationGainStats)] = {
+      bins: Array[Array[Bin]])
+    : Array[(Split, InformationGainStats)] = {
 
     //Common calculations for multiple nested methods
     val numNodes = scala.math.pow(2, level).toInt
@@ -301,7 +302,8 @@ object DecisionTree extends Serializable with Logging {
     def findBin(
         featureIndex: Int,
         labeledPoint: LabeledPoint,
-        isFeatureContinuous: Boolean): Int = {
+        isFeatureContinuous: Boolean)
+      : Int = {
 
       if (isFeatureContinuous){
         for (binIndex <- 0 until strategy.numBins) {
@@ -515,7 +517,8 @@ object DecisionTree extends Serializable with Logging {
         featureIndex: Int,
         splitIndex: Int,
         rightNodeAgg: Array[Array[Double]],
-        topImpurity: Double): InformationGainStats = {
+        topImpurity: Double)
+      : InformationGainStats = {
 
       strategy.algo match {
         case Classification => {
@@ -694,7 +697,8 @@ object DecisionTree extends Serializable with Logging {
     def calculateGainsForAllNodeSplits(
         leftNodeAgg: Array[Array[Double]],
         rightNodeAgg: Array[Array[Double]],
-        nodeImpurity: Double): Array[Array[InformationGainStats]] = {
+        nodeImpurity: Double)
+      : Array[Array[InformationGainStats]] = {
 
       val gains = Array.ofDim[InformationGainStats](numFeatures, numBins - 1)
 
@@ -715,7 +719,8 @@ object DecisionTree extends Serializable with Logging {
      */
     def binsToBestSplit(
         binData: Array[Double],
-        nodeImpurity: Double): (Split, InformationGainStats) = {
+        nodeImpurity: Double)
+      : (Split, InformationGainStats) = {
 
       logDebug("node impurity = " + nodeImpurity)
       val (leftNodeAgg, rightNodeAgg) = extractLeftRightNodeAggregates(binData)
@@ -786,7 +791,8 @@ object DecisionTree extends Serializable with Logging {
    */
   def findSplitsBins(
       input: RDD[LabeledPoint],
-      strategy: Strategy): (Array[Array[Split]], Array[Array[Bin]]) = {
+      strategy: Strategy)
+    : (Array[Array[Split]], Array[Array[Bin]]) = {
 
     val count = input.count()
 
@@ -947,12 +953,11 @@ object DecisionTree extends Serializable with Logging {
     }
     val options = nextOption(Map(),arglist)
     logDebug(options.toString())
-    //TODO: Add validation for input parameters
 
     //Load training data
     val trainData = loadLabeledData(sc, options.get('trainDataDir).get.toString)
 
-    //Figure out the type of algorithm
+    //Identify the type of algorithm
     val algoStr =  options.get('algo).get.toString
     val algo = algoStr match {
       case "Classification" => Classification
@@ -1007,7 +1012,10 @@ object DecisionTree extends Serializable with Logging {
     }
   }
 
-  //TODO: Port them to a metrics package
+  //TODO: Port this method to a generic metrics package
+  /**
+   * Calculates the classifier accuracy.
+   */
   def accuracyScore(model: DecisionTreeModel, data: RDD[LabeledPoint]): Double = {
     val correctCount = data.filter(y => model.predict(y.features) == y.label).count()
     val count = data.count()
@@ -1016,7 +1024,10 @@ object DecisionTree extends Serializable with Logging {
     correctCount.toDouble / count
   }
 
-  //TODO: Make these generic MLTable metrics
+  //TODO: Port this method to a generic metrics package
+  /**
+   * Calculates the mean squared error for regression
+   */
   def meanSquaredError(tree: DecisionTreeModel, data: RDD[LabeledPoint]): Double = {
     val meanSumOfSquares =
       data.map(y => (tree.predict(y.features) - y.label)*(tree.predict(y.features) - y.label))