Minor

Author: MechCoder

mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala

@@ -47,16 +47,8 @@ object AbsoluteError extends Loss {
     if ((point.label - model.predict(point.features)) < 0) 1.0 else -1.0
   }
 
-  /**
-   * Method to calculate loss when the predictions are already known.
-   * Note: This method is used in the method evaluateEachIteration to avoid recomputing the
-   * predicted values from previously fit trees.
-   * @param prediction Predicted label.
-   * @param datum LabeledPoint.
-   * @return Absolute error of model on the given datapoint.
-   */
-  override def computeError(prediction: Double, datum: LabeledPoint): Double = {
-    val err = datum.label - prediction
+  override def computeError(prediction: Double, label: Double): Double = {
+    val err = label - prediction
     math.abs(err)
   }
 

mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala

@@ -50,16 +50,8 @@ object LogLoss extends Loss {
     - 4.0 * point.label / (1.0 + math.exp(2.0 * point.label * prediction))
   }
 
-  /**
-   * Method to calculate loss when the predictions are already known.
-   * Note: This method is used in the method evaluateEachIteration to avoid recomputing the
-   * predicted values from previously fit trees.
-   * @param prediction Predicted label.
-   * @param datum LabeledPoint
-   * @return log loss of model on the datapoint.
-   */
-  override def computeError(prediction: Double, datum: LabeledPoint): Double = {
-    val margin = 2.0 * datum.label * prediction
+  override def computeError(prediction: Double, label: Double): Double = {
+    val margin = 2.0 * label * prediction
     // The following is equivalent to 2.0 * log(1 + exp(-margin)) but more numerically stable.
     2.0 * MLUtils.log1pExp(-margin)
   }

mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala

@@ -48,17 +48,17 @@ trait Loss extends Serializable {
    * @return Measure of model error on data
    */
   def computeError(model: TreeEnsembleModel, data: RDD[LabeledPoint]): Double = {
-    data.map(point => computeError(model.predict(point.features), point)).mean()
+    data.map(point => computeError(model.predict(point.features), point.label)).mean()
   }
 
   /**
    * Method to calculate loss when the predictions are already known.
    * Note: This method is used in the method evaluateEachIteration to avoid recomputing the
    * predicted values from previously fit trees.
    * @param prediction Predicted label.
-   * @param datum LabeledPoint
+   * @param label True label.
    * @return Measure of model error on datapoint.
    */
-  def computeError(prediction: Double, datum: LabeledPoint): Double
+  def computeError(prediction: Double, label: Double): Double
 
 }

mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala

@@ -47,16 +47,8 @@ object SquaredError extends Loss {
     2.0 * (model.predict(point.features) - point.label)
   }
 
-  /**
-   * Method to calculate loss when the predictions are already known.
-   * Note: This method is used in the method evaluateEachIteration to avoid recomputing the
-   * predicted values from previously fit trees.
-   * @param prediction Predicted label.
-   * @param datum LabeledPoint
-   * @return Mean squared error of model on datapoint.
-   */
-  override def computeError(prediction: Double, datum: LabeledPoint): Double = {
-    val err = prediction - datum.label
+  override def computeError(prediction: Double, label: Double): Double = {
+    val err = prediction - label
     err * err
   }
 

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

@@ -133,7 +133,7 @@ class GradientBoostedTreesModel(
 
     var predictionAndError: RDD[(Double, Double)] = remappedData.map { i =>
       val pred = treeWeights(0) * trees(0).predict(i.features)
-      val error = loss.computeError(pred, i)
+      val error = loss.computeError(pred, i.label)
       (pred, error)
     }
     evaluationArray(0) = predictionAndError.values.mean()
@@ -143,13 +143,13 @@ class GradientBoostedTreesModel(
     val broadcastWeights = sc.broadcast(treeWeights)
 
     (1 until numIterations).map { nTree =>
-      val currentTree = broadcastTrees.value(nTree)
-      val currentTreeWeight = broadcastWeights.value(nTree)
       predictionAndError = remappedData.zip(predictionAndError).mapPartitions { iter =>
-        iter map {
+        val currentTree = broadcastTrees.value(nTree)
+        val currentTreeWeight = broadcastWeights.value(nTree)
+        iter.map {
           case (point, (pred, error)) => {
             val newPred = pred + currentTree.predict(point.features) * currentTreeWeight
-            val newError = loss.computeError(newPred, point)
+            val newError = loss.computeError(newPred, point.label)
             (newPred, newError)
           }
         }