minor

Author: MechCoder

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

@@ -130,17 +130,18 @@ class GradientBoostedTreesModel(
 
     val numIterations = trees.length
     val evaluationArray = Array.fill(numIterations)(0.0)
+    val localTreeWeights = treeWeights
 
     var predictionAndError = GradientBoostedTreesModel.computeInitialPredictionAndError(
-      remappedData, treeWeights(0), trees(0), loss)
+      remappedData, localTreeWeights(0), trees(0), loss)
 
     evaluationArray(0) = predictionAndError.values.mean()
 
     val broadcastTrees = sc.broadcast(trees)
     (1 until numIterations).map { nTree =>
       predictionAndError = remappedData.zip(predictionAndError).mapPartitions { iter =>
         val currentTree = broadcastTrees.value(nTree)
-        val currentTreeWeight = treeWeights(nTree)
+        val currentTreeWeight = localTreeWeights(nTree)
         iter.map { case (point, (pred, error)) =>
           val newPred = pred + currentTree.predict(point.features) * currentTreeWeight
           val newError = loss.computeError(newPred, point.label)
@@ -198,8 +199,6 @@ object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] {
     tree: DecisionTreeModel,
     loss: Loss): RDD[(Double, Double)] = {
 
-    val sc = data.sparkContext
-
     val newPredError = data.zip(predictionAndError).mapPartitions { iter =>
       iter.map {
         case (lp, (pred, error)) => {