Removed WeightedLabeledPoint. Replaced by tuple of doubles

Author: zapletal-martin

mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala

@@ -66,7 +66,7 @@ object MonotonicityConstraint {
  * @param monotonicityConstraint specifies if the sequence is increasing or decreasing
  */
 class IsotonicRegressionModel(
-    val predictions: Seq[WeightedLabeledPoint],
+    val predictions: Seq[(Double, Double, Double)],
     val monotonicityConstraint: MonotonicityConstraint)
   extends RegressionModel {
 
@@ -76,7 +76,7 @@ class IsotonicRegressionModel(
   override def predict(testData: Vector): Double = {
     // Take the highest of data points smaller than our feature or data point with lowest feature
     (predictions.head +:
-      predictions.filter(y => y.features.toArray.head <= testData.toArray.head)).last.label
+      predictions.filter(y => y._2 <= testData.toArray.head)).last._1
   }
 }
 
@@ -95,7 +95,7 @@ trait IsotonicRegressionAlgorithm
    * @return isotonic regression model
    */
   protected def createModel(
-      predictions: Seq[WeightedLabeledPoint],
+      predictions: Seq[(Double, Double, Double)],
       monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel
 
   /**
@@ -106,7 +106,7 @@ trait IsotonicRegressionAlgorithm
    * @return isotonic regression model
    */
   def run(
-      input: RDD[WeightedLabeledPoint],
+      input: RDD[(Double, Double, Double)],
       monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel
 }
 
@@ -117,15 +117,15 @@ class PoolAdjacentViolators private [mllib]
   extends IsotonicRegressionAlgorithm {
 
   override def run(
-      input: RDD[WeightedLabeledPoint],
+      input: RDD[(Double, Double, Double)],
       monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel = {
     createModel(
       parallelPoolAdjacentViolators(input, monotonicityConstraint),
       monotonicityConstraint)
   }
 
   override protected def createModel(
-      predictions: Seq[WeightedLabeledPoint],
+      predictions: Seq[(Double, Double, Double)],
       monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel = {
     new IsotonicRegressionModel(predictions, monotonicityConstraint)
   }
@@ -194,12 +194,12 @@ class PoolAdjacentViolators private [mllib]
    * @return result
    */
   private def parallelPoolAdjacentViolators(
-      testData: RDD[WeightedLabeledPoint],
-      monotonicityConstraint: MonotonicityConstraint): Seq[WeightedLabeledPoint] = {
+      testData: RDD[(Double, Double, Double)],
+      monotonicityConstraint: MonotonicityConstraint): Seq[(Double, Double, Double)] = {
 
     poolAdjacentViolators(
       testData
-        .sortBy(_.features.toArray.head)
+        .sortBy(_._2)
         .cache()
         .mapPartitions(it => poolAdjacentViolators(it.toArray, monotonicityConstraint).toIterator)
         .collect(), monotonicityConstraint)
@@ -224,7 +224,7 @@ object IsotonicRegression {
    * @param monotonicityConstraint Isotonic (increasing) or Antitonic (decreasing) sequence
    */
   def train(
-      input: RDD[WeightedLabeledPoint],
+      input: RDD[(Double, Double, Double)],
       monotonicityConstraint: MonotonicityConstraint = Isotonic): IsotonicRegressionModel = {
     new PoolAdjacentViolators().run(input, monotonicityConstraint)
   }