From e006f9d5914b28b30aa8c24b0d1ff9977f23179e Mon Sep 17 00:00:00 2001
From: Manish Amde <manish9ue@gmail.com>
Date: Sun, 11 May 2014 21:29:47 -0700
Subject: [PATCH] changing variable names

---
 .../spark/mllib/tree/DecisionTree.scala       | 25 +++++++++++--------
 1 file changed, 14 insertions(+), 11 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index 3da92ed891611..52ae362028f5c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -46,18 +46,15 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
    * @param input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as training data
    * @return a DecisionTreeModel that can be used for prediction
    */
-  def train(input: RDD[LabeledPoint]): DecisionTreeModel = {
-
-    // Converting from standard instance format to weighted input format for tree training
-    val weightedInput = input.map(x => WeightedLabeledPoint(x.label,x.features))
+  def train(input: RDD[WeightedLabeledPoint]): DecisionTreeModel = {
 
     // Cache input RDD for speedup during multiple passes.
-    weightedInput.cache()
+    input.cache()
     logDebug("algo = " + strategy.algo)
 
     // Find the splits and the corresponding bins (interval between the splits) using a sample
     // of the input data.
-    val (splits, bins) = DecisionTree.findSplitsBins(weightedInput, strategy)
+    val (splits, bins) = DecisionTree.findSplitsBins(input, strategy)
     val numBins = bins(0).length
     logDebug("numBins = " + numBins)
 
@@ -74,7 +71,7 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
     // dummy value for top node (updated during first split calculation)
     val nodes = new Array[Node](maxNumNodes)
     // num features
-    val numFeatures = weightedInput.take(1)(0).features.size
+    val numFeatures = input.take(1)(0).features.size
 
     // Calculate level for single group construction
 
@@ -113,7 +110,7 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
       logDebug("#####################################")
 
       // Find best split for all nodes at a level.
-      val splitsStatsForLevel = DecisionTree.findBestSplits(weightedInput, parentImpurities,
+      val splitsStatsForLevel = DecisionTree.findBestSplits(input, parentImpurities,
         strategy, level, filters, splits, bins, maxLevelForSingleGroup)
 
       for ((nodeSplitStats, index) <- splitsStatsForLevel.view.zipWithIndex) {
@@ -216,7 +213,9 @@ object DecisionTree extends Serializable with Logging {
    * @return a DecisionTreeModel that can be used for prediction
   */
   def train(input: RDD[LabeledPoint], strategy: Strategy): DecisionTreeModel = {
-    new DecisionTree(strategy).train(input: RDD[LabeledPoint])
+    // Converting from standard instance format to weighted input format for tree training
+    val weightedInput = input.map(x => WeightedLabeledPoint(x.label,x.features))
+    new DecisionTree(strategy).train(weightedInput: RDD[WeightedLabeledPoint])
   }
 
   /**
@@ -238,7 +237,9 @@ object DecisionTree extends Serializable with Logging {
       impurity: Impurity,
       maxDepth: Int): DecisionTreeModel = {
     val strategy = new Strategy(algo,impurity,maxDepth)
-    new DecisionTree(strategy).train(input: RDD[LabeledPoint])
+    // Converting from standard instance format to weighted input format for tree training
+    val weightedInput = input.map(x => WeightedLabeledPoint(x.label,x.features))
+    new DecisionTree(strategy).train(weightedInput: RDD[WeightedLabeledPoint])
   }
 
 
@@ -273,7 +274,9 @@ object DecisionTree extends Serializable with Logging {
       categoricalFeaturesInfo: Map[Int,Int]): DecisionTreeModel = {
     val strategy = new Strategy(algo, impurity, maxDepth, maxBins, quantileCalculationStrategy,
       categoricalFeaturesInfo)
-    new DecisionTree(strategy).train(input: RDD[LabeledPoint])
+    // Converting from standard instance format to weighted input format for tree training
+    val weightedInput = input.map(x => WeightedLabeledPoint(x.label,x.features))
+    new DecisionTree(strategy).train(weightedInput: RDD[WeightedLabeledPoint])
   }
 
   private val InvalidBinIndex = -1