.view -> .iterator

apache · Feb 17, 2020 · 54d6fc3 · 54d6fc3
1 parent b261e23
commit 54d6fc3
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Showing 6 changed files with 10 additions and 10 deletions.
diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
@@ -139,7 +139,7 @@ class Pipeline @Since("1.4.0") (
     val theStages = $(stages)
     // Search for the last estimator.
     var indexOfLastEstimator = -1
-    theStages.view.zipWithIndex.foreach { case (stage, index) =>
+    theStages.iterator.zipWithIndex.foreach { case (stage, index) =>
       stage match {
         case _: Estimator[_] =>
           indexOfLastEstimator = index
@@ -148,7 +148,7 @@ class Pipeline @Since("1.4.0") (
     }
     var curDataset = dataset
     val transformers = ListBuffer.empty[Transformer]
-    theStages.view.zipWithIndex.foreach { case (stage, index) =>
+    theStages.iterator.zipWithIndex.foreach { case (stage, index) =>
       if (index <= indexOfLastEstimator) {
         val transformer = stage match {
           case estimator: Estimator[_] =>

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -256,7 +256,7 @@ class RandomForestClassificationModel private[ml] (
     // Classifies using majority votes.
     // Ignore the tree weights since all are 1.0 for now.
     val votes = Array.ofDim[Double](numClasses)
-    _trees.view.foreach { tree =>
+    _trees.foreach { tree =>
       val classCounts = tree.rootNode.predictImpl(features).impurityStats.stats
       val total = classCounts.sum
       if (total != 0) {

diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
@@ -1049,7 +1049,7 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
       .join(userFactors)
       .mapPartitions({ items =>
         items.flatMap { case (_, (ids, factors)) =>
-          ids.view.zip(factors)
+          ids.iterator.zip(factors.iterator)
         }
       // Preserve the partitioning because IDs are consistent with the partitioners in userInBlocks
       // and userFactors.
@@ -1061,7 +1061,7 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
       .join(itemFactors)
       .mapPartitions({ items =>
         items.flatMap { case (_, (ids, factors)) =>
-          ids.view.zip(factors)
+          ids.iterator.zip(factors.iterator)
         }
       }, preservesPartitioning = true)
       .setName("itemFactors")
@@ -1376,7 +1376,7 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
           Iterator.empty
         }
       } ++ {
-        builders.view.zipWithIndex.filter(_._1.size > 0).map { case (block, idx) =>
+        builders.iterator.zipWithIndex.filter(_._1.size > 0).map { case (block, idx) =>
           val srcBlockId = idx % srcPart.numPartitions
           val dstBlockId = idx / srcPart.numPartitions
           ((srcBlockId, dstBlockId), block.build())
@@ -1695,7 +1695,7 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
     val YtY = if (implicitPrefs) Some(computeYtY(srcFactorBlocks, rank)) else None
     val srcOut = srcOutBlocks.join(srcFactorBlocks).flatMap {
       case (srcBlockId, (srcOutBlock, srcFactors)) =>
-        srcOutBlock.view.zipWithIndex.map { case (activeIndices, dstBlockId) =>
+        srcOutBlock.iterator.zipWithIndex.map { case (activeIndices, dstBlockId) =>
           (dstBlockId, (srcBlockId, activeIndices.map(idx => srcFactors(idx))))
         }
     }

diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
@@ -825,7 +825,7 @@ private[spark] object RandomForest extends Logging with Serializable {
     }
 
     val validFeatureSplits =
-      Range(0, binAggregates.metadata.numFeaturesPerNode).view.map { featureIndexIdx =>
+      Iterator.range(0, binAggregates.metadata.numFeaturesPerNode).map { featureIndexIdx =>
         featuresForNode.map(features => (featureIndexIdx, features(featureIndexIdx)))
           .getOrElse((featureIndexIdx, featureIndexIdx))
       }.withFilter { case (_, featureIndex) =>

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
@@ -95,7 +95,7 @@ class StreamingKMeansModel @Since("1.2.0") (
     val discount = timeUnit match {
       case StreamingKMeans.BATCHES => decayFactor
       case StreamingKMeans.POINTS =>
-        val numNewPoints = pointStats.view.map { case (_, (_, n)) =>
+        val numNewPoints = pointStats.iterator.map { case (_, (_, n)) =>
           n
         }.sum
         math.pow(decayFactor, numNewPoints)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
@@ -292,7 +292,7 @@ private[tree] sealed class TreeEnsembleModel(
    */
   private def predictByVoting(features: Vector): Double = {
     val votes = mutable.Map.empty[Int, Double]
-    trees.view.zip(treeWeights).foreach { case (tree, weight) =>
+    trees.iterator.zip(treeWeights.iterator).foreach { case (tree, weight) =>
       val prediction = tree.predict(features).toInt
       votes(prediction) = votes.getOrElse(prediction, 0.0) + weight
     }