update DecisionTree to use RDD[Vector]

mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala

@@ -151,6 +151,8 @@ class DenseVector(val values: Array[Double]) extends Vector {
   override def toArray: Array[Double] = values
 
   private[mllib] override def toBreeze: BV[Double] = new BDV[Double](values)
+
+  override def apply(i: Int) = values(i)
 }
 
 /**

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

@@ -30,6 +30,7 @@ import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Impurity, Variance}
 import org.apache.spark.mllib.tree.model._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.random.XORShiftRandom
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
 
 /**
  * A class that implements a decision tree algorithm for classification and regression. It
@@ -295,7 +296,7 @@ object DecisionTree extends Serializable with Logging {
     val numNodes = scala.math.pow(2, level).toInt
     logDebug("numNodes = " + numNodes)
     // Find the number of features by looking at the first sample.
-    val numFeatures = input.first().features.length
+    val numFeatures = input.first().features.size
     logDebug("numFeatures = " + numFeatures)
     val numBins = bins(0).length
     logDebug("numBins = " + numBins)
@@ -902,7 +903,7 @@ object DecisionTree extends Serializable with Logging {
     val count = input.count()
 
     // Find the number of features by looking at the first sample
-    val numFeatures = input.take(1)(0).features.length
+    val numFeatures = input.take(1)(0).features.size
 
     val maxBins = strategy.maxBins
     val numBins = if (maxBins <= count) maxBins else count.toInt
@@ -1116,7 +1117,7 @@ object DecisionTree extends Serializable with Logging {
     sc.textFile(dir).map { line =>
       val parts = line.trim().split(",")
       val label = parts(0).toDouble
-      val features = parts.slice(1,parts.length).map(_.toDouble)
+      val features = Vectors.dense(parts.slice(1,parts.length).map(_.toDouble))
       LabeledPoint(label, features)
     }
   }
@@ -1127,7 +1128,7 @@ object DecisionTree extends Serializable with Logging {
    */
   private def accuracyScore(model: DecisionTreeModel, data: RDD[LabeledPoint],
       threshold: Double = 0.5): Double = {
-    def predictedValue(features: Array[Double]) = {
+    def predictedValue(features: Vector) = {
       if (model.predict(features) < threshold) 0.0 else 1.0
     }
     val correctCount = data.filter(y => predictedValue(y.features) == y.label).count()

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

@@ -19,6 +19,7 @@ package org.apache.spark.mllib.tree.model
 
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.Vector
 
 /**
  * Model to store the decision tree parameters
@@ -33,7 +34,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
    * @param features array representing a single data point
    * @return Double prediction from the trained model
    */
-  def predict(features: Array[Double]): Double = {
+  def predict(features: Vector): Double = {
     topNode.predictIfLeaf(features)
   }
 
@@ -43,7 +44,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
    * @param features RDD representing data points to be predicted
    * @return RDD[Int] where each entry contains the corresponding prediction
    */
-  def predict(features: RDD[Array[Double]]): RDD[Double] = {
+  def predict(features: RDD[Vector]): RDD[Double] = {
     features.map(x => predict(x))
   }
 }

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

@@ -19,6 +19,7 @@ package org.apache.spark.mllib.tree.model
 
 import org.apache.spark.Logging
 import org.apache.spark.mllib.tree.configuration.FeatureType._
+import org.apache.spark.mllib.linalg.Vector
 
 /**
  * Node in a decision tree
@@ -54,8 +55,8 @@ class Node (
     logDebug("stats = " + stats)
     logDebug("predict = " + predict)
     if (!isLeaf) {
-      val leftNodeIndex = id*2 + 1
-      val rightNodeIndex = id*2 + 2
+      val leftNodeIndex = id * 2 + 1
+      val rightNodeIndex = id * 2 + 2
       leftNode = Some(nodes(leftNodeIndex))
       rightNode = Some(nodes(rightNodeIndex))
       leftNode.get.build(nodes)
@@ -68,7 +69,7 @@ class Node (
    * @param feature feature value
    * @return predicted value
    */
-  def predictIfLeaf(feature: Array[Double]) : Double = {
+  def predictIfLeaf(feature: Vector) : Double = {
     if (isLeaf) {
       predict
     } else{

mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala

@@ -27,6 +27,7 @@ import org.apache.spark.mllib.tree.model.Filter
 import org.apache.spark.mllib.tree.configuration.Strategy
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.configuration.FeatureType._
+import org.apache.spark.mllib.linalg.Vectors
 
 class DecisionTreeSuite extends FunSuite with BeforeAndAfterAll {
 
@@ -396,7 +397,7 @@ object DecisionTreeSuite {
   def generateOrderedLabeledPointsWithLabel0(): Array[LabeledPoint] = {
     val arr = new Array[LabeledPoint](1000)
     for (i <- 0 until 1000){
-      val lp = new LabeledPoint(0.0,Array(i.toDouble,1000.0-i))
+      val lp = new LabeledPoint(0.0, Vectors.dense(i.toDouble, 1000.0 - i))
       arr(i) = lp
     }
     arr
@@ -405,7 +406,7 @@ object DecisionTreeSuite {
   def generateOrderedLabeledPointsWithLabel1(): Array[LabeledPoint] = {
     val arr = new Array[LabeledPoint](1000)
     for (i <- 0 until 1000){
-      val lp = new LabeledPoint(1.0,Array(i.toDouble,999.0-i))
+      val lp = new LabeledPoint(1.0, Vectors.dense(i.toDouble, 999.0 - i))
       arr(i) = lp
     }
     arr
@@ -415,9 +416,9 @@ object DecisionTreeSuite {
     val arr = new Array[LabeledPoint](1000)
     for (i <- 0 until 1000){
       if (i < 600){
-        arr(i) = new LabeledPoint(1.0,Array(0.0,1.0))
+        arr(i) = new LabeledPoint(1.0, Vectors.dense(0.0, 1.0))
       } else {
-        arr(i) = new LabeledPoint(0.0,Array(1.0,0.0))
+        arr(i) = new LabeledPoint(0.0, Vectors.dense(1.0, 0.0))
       }
     }
     arr