[MINOR][ML] ML cleanup

### What changes were proposed in this pull request?
1, remove used imports and variables;
2, use `.iterator` instead of `.view` to avoid IDEA warnings;
3, remove resolved _TODO_

### Why are the changes needed?
cleanup

### Does this PR introduce any user-facing change?
No

### How was this patch tested?
existing testsuites

Closes #27600 from zhengruifeng/nits.

Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>

mllib-local/src/main/scala/org/apache/spark/ml/linalg/BLAS.scala

@@ -682,7 +682,6 @@ private[spark] object BLAS extends Serializable {
 
           val xTemp = xValues(k) * alpha
           while (i < indEnd) {
-            val rowIndex = Arows(i)
             yValues(Arows(i)) += Avals(i) * xTemp
             i += 1
           }
@@ -734,8 +733,7 @@ private[spark] object BLAS extends Serializable {
         val indEnd = Acols(colCounterForA + 1)
         val xVal = xValues(colCounterForA) * alpha
         while (i < indEnd) {
-          val rowIndex = Arows(i)
+          yValues(Arows(i)) += Avals(i) * xVal
-          yValues(rowIndex) += Avals(i) * xVal
           i += 1
         }
         colCounterForA += 1

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[_] =>

mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala

@@ -67,14 +67,12 @@ class AttributeGroup private (
   /**
    * Optional array of attributes. At most one of `numAttributes` and `attributes` can be defined.
    */
-  val attributes: Option[Array[Attribute]] = attrs.map(_.view.zipWithIndex.map { case (attr, i) =>
+  val attributes: Option[Array[Attribute]] = attrs.map(_.iterator.zipWithIndex
-    attr.withIndex(i)
+    .map { case (attr, i) => attr.withIndex(i) }.toArray)
-  }.toArray)
 
   private lazy val nameToIndex: Map[String, Int] = {
-    attributes.map(_.view.flatMap { attr =>
+    attributes.map(_.iterator.flatMap { attr => attr.name.map(_ -> attr.index.get)}.toMap)
-      attr.name.map(_ -> attr.index.get)
+      .getOrElse(Map.empty)
-    }.toMap).getOrElse(Map.empty)
   }
 
   /** Size of the attribute group. Returns -1 if the size is unknown. */

mllib/src/main/scala/org/apache/spark/ml/classification/FMClassifier.scala

@@ -31,7 +31,6 @@ import org.apache.spark.mllib.linalg.{Vector => OldVector}
 import org.apache.spark.mllib.linalg.VectorImplicits._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{Dataset, Row}
-import org.apache.spark.sql.functions.col
 import org.apache.spark.storage.StorageLevel
 
 /**

mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala

@@ -17,8 +17,6 @@
 
 package org.apache.spark.ml.classification
 
-import scala.collection.JavaConverters._
-
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.annotation.Since

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) {

mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala

@@ -21,7 +21,7 @@ import org.apache.spark.annotation.Since
 import org.apache.spark.ml.linalg.{Vector, VectorUDT}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
-import org.apache.spark.ml.util.{DefaultParamsReadable, DefaultParamsWritable, Identifiable, SchemaUtils}
+import org.apache.spark.ml.util.{DefaultParamsReadable, DefaultParamsWritable, Identifiable, MetadataUtils, SchemaUtils}
 import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
 import org.apache.spark.sql.{Dataset, Row}
 import org.apache.spark.sql.functions._
@@ -104,7 +104,9 @@ class BinaryClassificationEvaluator @Since("1.4.0") (@Since("1.4.0") override va
       SchemaUtils.checkNumericType(schema, $(weightCol))
     }
 
-    // TODO: When dataset metadata has been implemented, check rawPredictionCol vector length = 2.
+    MetadataUtils.getNumFeatures(schema($(rawPredictionCol)))
+      .foreach(n => require(n == 2, s"rawPredictionCol vectors must have length=2, but got $n"))
+
     val scoreAndLabelsWithWeights =
       dataset.select(
         col($(rawPredictionCol)),

mllib/src/main/scala/org/apache/spark/ml/evaluation/ClusteringEvaluator.scala

@@ -20,7 +20,7 @@ package org.apache.spark.ml.evaluation
 import org.apache.spark.SparkContext
 import org.apache.spark.annotation.Since
 import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.ml.linalg.{BLAS, DenseVector, SparseVector, Vector, Vectors}
+import org.apache.spark.ml.linalg.{BLAS, DenseVector, Vector, Vectors}
 import org.apache.spark.ml.param.{Param, ParamMap, ParamValidators}
 import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasPredictionCol}
 import org.apache.spark.ml.util._

mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala

@@ -144,8 +144,6 @@ object MinMaxScaler extends DefaultParamsReadable[MinMaxScaler] {
  *
  * @param originalMin min value for each original column during fitting
  * @param originalMax max value for each original column during fitting
- *
- * TODO: The transformer does not yet set the metadata in the output column (SPARK-8529).
  */
 @Since("1.5.0")
 class MinMaxScalerModel private[ml] (

mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala

@@ -25,7 +25,6 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.ml.{Pipeline, PipelineModel}
-import org.apache.spark.ml.attribute.AttributeGroup
 import org.apache.spark.ml.feature.RFormula
 import org.apache.spark.ml.r.RWrapperUtils._
 import org.apache.spark.ml.regression._

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))))
         }
     }

mllib/src/main/scala/org/apache/spark/ml/regression/FMRegressor.scala

@@ -40,7 +40,6 @@ import org.apache.spark.mllib.optimization.{Gradient, GradientDescent, SquaredL2
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{Dataset, Row}
-import org.apache.spark.sql.functions.col
 import org.apache.spark.storage.StorageLevel
 
 /**

mllib/src/main/scala/org/apache/spark/ml/source/image/ImageFileFormat.scala

@@ -26,9 +26,8 @@ import org.apache.spark.ml.image.ImageSchema
 import org.apache.spark.sql.SparkSession
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.encoders.RowEncoder
-import org.apache.spark.sql.catalyst.expressions.{AttributeReference, UnsafeRow}
+import org.apache.spark.sql.catalyst.expressions.UnsafeRow
-import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
+import org.apache.spark.sql.execution.datasources.{FileFormat, OutputWriterFactory, PartitionedFile}
-import org.apache.spark.sql.execution.datasources.{DataSource, FileFormat, OutputWriterFactory, PartitionedFile}
 import org.apache.spark.sql.sources.{DataSourceRegister, Filter}
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.util.SerializableConfiguration

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) =>

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)
@@ -125,9 +125,8 @@ class StreamingKMeansModel @Since("1.2.0") (
     }
 
     // Check whether the smallest cluster is dying. If so, split the largest cluster.
-    val weightsWithIndex = clusterWeights.view.zipWithIndex
+    val (maxWeight, largest) = clusterWeights.iterator.zipWithIndex.maxBy(_._1)
-    val (maxWeight, largest) = weightsWithIndex.maxBy(_._1)
+    val (minWeight, smallest) = clusterWeights.iterator.zipWithIndex.minBy(_._1)
-    val (minWeight, smallest) = weightsWithIndex.minBy(_._1)
     if (minWeight < 1e-8 * maxWeight) {
       logInfo(s"Cluster $smallest is dying. Split the largest cluster $largest into two.")
       val weight = (maxWeight + minWeight) / 2.0

mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala

@@ -74,8 +74,9 @@ class ChiSqSelectorModel @Since("1.3.0") (
     }
   }
 
-  private[spark] def compressSparse(indices: Array[Int],
+  private[spark] def compressSparse(
-                                    values: Array[Double]): (Array[Int], Array[Double]) = {
+      indices: Array[Int],
+      values: Array[Double]): (Array[Int], Array[Double]) = {
     val newValues = new ArrayBuilder.ofDouble
     val newIndices = new ArrayBuilder.ofInt
     var i = 0

mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala

@@ -64,8 +64,9 @@ class ElementwiseProduct @Since("1.4.0") (
     newValues
   }
 
-  private[spark] def transformSparse(indices: Array[Int],
+  private[spark] def transformSparse(
-                                     values: Array[Double]): (Array[Int], Array[Double]) = {
+      indices: Array[Int],
+      values: Array[Double]): (Array[Int], Array[Double]) = {
     val newValues = values.clone()
     val dim = newValues.length
     var i = 0

mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala

@@ -226,8 +226,9 @@ private[spark] object IDFModel {
     }
   }
 
-  private[spark] def transformDense(idf: Vector,
+  private[spark] def transformDense(
-                                    values: Array[Double]): Array[Double] = {
+      idf: Vector,
+      values: Array[Double]): Array[Double] = {
     val n = values.length
     val newValues = new Array[Double](n)
     var j = 0
@@ -238,9 +239,10 @@ private[spark] object IDFModel {
     newValues
   }
 
-  private[spark] def transformSparse(idf: Vector,
+  private[spark] def transformSparse(
-                                     indices: Array[Int],
+      idf: Vector,
-                                     values: Array[Double]): (Array[Int], Array[Double]) = {
+      indices: Array[Int],
+      values: Array[Double]): (Array[Int], Array[Double]) = {
     val nnz = indices.length
     val newValues = new Array[Double](nnz)
     var k = 0

mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala

@@ -663,7 +663,6 @@ private[spark] object BLAS extends Serializable with Logging {
 
           val xTemp = xValues(k) * alpha
           while (i < indEnd) {
-            val rowIndex = Arows(i)
             yValues(Arows(i)) += Avals(i) * xTemp
             i += 1
           }
@@ -715,8 +714,7 @@ private[spark] object BLAS extends Serializable with Logging {
         val indEnd = Acols(colCounterForA + 1)
         val xVal = xValues(colCounterForA) * alpha
         while (i < indEnd) {
-          val rowIndex = Arows(i)
+          yValues(Arows(i)) += Avals(i) * xVal
-          yValues(rowIndex) += Avals(i) * xVal
           i += 1
         }
         colCounterForA += 1

mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala

@@ -292,7 +292,7 @@ object GradientDescent extends Logging {
       miniBatchFraction: Double,
       initialWeights: Vector): (Vector, Array[Double]) =
     GradientDescent.runMiniBatchSGD(data, gradient, updater, stepSize, numIterations,
-                                    regParam, miniBatchFraction, initialWeights, 0.001)
+      regParam, miniBatchFraction, initialWeights, 0.001)
 
 
   private def isConverged(

mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/SpearmanCorrelation.scala

@@ -46,9 +46,7 @@ private[stat] object SpearmanCorrelation extends Correlation with Logging {
   override def computeCorrelationMatrix(X: RDD[Vector]): Matrix = {
     // ((columnIndex, value), rowUid)
     val colBased = X.zipWithUniqueId().flatMap { case (vec, uid) =>
-      vec.toArray.view.zipWithIndex.map { case (v, j) =>
+      vec.iterator.map(t => (t, uid))
-        ((j, v), uid)
-      }
     }
     // global sort by (columnIndex, value)
     val sorted = colBased.sortByKey()

mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala

@@ -25,7 +25,9 @@ import org.apache.spark.annotation.{DeveloperApi, Since}
 @Since("1.0.0")
 object Entropy extends Impurity {
 
-  private[tree] def log2(x: Double) = scala.math.log(x) / scala.math.log(2)
+  private val _log2 = scala.math.log(2)
+
+  private[tree] def log2(x: Double) = scala.math.log(x) / _log2
 
   /**
    * :: DeveloperApi ::

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
     }

mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala

@@ -20,8 +20,6 @@ package org.apache.spark.mllib.util
 import scala.collection.JavaConverters._
 import scala.util.Random
 
-import com.github.fommil.netlib.BLAS.{getInstance => blas}
-
 import org.apache.spark.SparkContext
 import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.linalg.{BLAS, Vectors}