minor modifications for code styling

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

@@ -107,9 +107,8 @@ class RandomForestClassifier @Since("1.4.0") (
     val numClasses: Int = getNumClasses(dataset)
     val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, numClasses)
     val strategy =
-      super.getOldStrategy(categoricalFeatures = categoricalFeatures, numClasses = numClasses,
-        oldAlgo = OldAlgo.Classification, oldImpurity = getOldImpurity,
-        subsamplingRate = getSubsamplingRate, classWeights = getClassWeights)
+      super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification,
+        getOldImpurity, getSubsamplingRate, getClassWeights)
 
     val instr = Instrumentation.create(this, oldDataset)
     instr.logParams(params: _*)

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

@@ -117,7 +117,7 @@ class DecisionTreeRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: S
   /** (private[ml]) Create a Strategy instance to use with the old API. */
   private[ml] def getOldStrategy(categoricalFeatures: Map[Int, Int]): OldStrategy = {
     super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, getOldImpurity,
-      1.0, Array())
+      subsamplingRate = 1.0, classWeights = Array())
   }
 
   @Since("1.4.0")

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

@@ -98,9 +98,8 @@ class RandomForestRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: S
       MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
     val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
     val strategy =
-      super.getOldStrategy(categoricalFeatures, numClasses = 0,
-        OldAlgo.Regression, getOldImpurity, getSubsamplingRate,
-        classWeights = Array())
+      super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression,
+        getOldImpurity, getSubsamplingRate, classWeights = Array())
 
     val instr = Instrumentation.create(this, oldDataset)
     instr.logParams(params: _*)

mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala

@@ -342,6 +342,8 @@ private[ml] object DecisionTreeModelReadWrite {
       Param.jsonDecode[String](compact(render(impurityJson)))
     }
 
+    // Get class weights to construct ImpurityCalculator. This value
+    // is ignored unless the impurity is WeightedGini
     val classWeights: Array[Double] = {
       val classWeightsJson: JValue = metadata.getParamValue("classWeights")
       compact(render(classWeightsJson)).split("\\[|,|\\]")
@@ -445,6 +447,8 @@ private[ml] object EnsembleModelReadWrite {
       Param.jsonDecode[String](compact(render(impurityJson)))
     }
 
+    // Get class weights to construct ImpurityCalculator. This value
+    // is ignored unless the impurity is WeightedGini
     val classWeights: Array[Double] = {
       val classWeightsJson: JValue = metadata.getParamValue("classWeights")
       val classWeightsArray = compact(render(classWeightsJson)).split("\\[|,|\\]")

mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala

@@ -155,7 +155,7 @@ private[ml] trait DecisionTreeParams extends PredictorParams
    */
   def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value)
 
-  /** (private[ml]) Create a Strategy instance to use with the old API. */
+  /** (private[ml]) Create a Strategy instance. */
   private[ml] def getOldStrategy(
       categoricalFeatures: Map[Int, Int],
       numClasses: Int,
@@ -181,25 +181,25 @@ private[ml] trait DecisionTreeParams extends PredictorParams
 
   /** (private[ml]) Create a Strategy whose interface is compatible with the old API. */
   private[ml] def getOldStrategy(
-    categoricalFeatures: Map[Int, Int],
-    numClasses: Int,
-    oldAlgo: OldAlgo.Algo,
-    oldImpurity: OldImpurity,
-    subsamplingRate: Double): OldStrategy = {
-  val strategy = OldStrategy.defaultStrategy(oldAlgo)
-  strategy.impurity = oldImpurity
-  strategy.checkpointInterval = getCheckpointInterval
-  strategy.maxBins = getMaxBins
-  strategy.maxDepth = getMaxDepth
-  strategy.maxMemoryInMB = getMaxMemoryInMB
-  strategy.minInfoGain = getMinInfoGain
-  strategy.minInstancesPerNode = getMinInstancesPerNode
-  strategy.useNodeIdCache = getCacheNodeIds
-  strategy.numClasses = numClasses
-  strategy.categoricalFeaturesInfo = categoricalFeatures
-  strategy.subsamplingRate = subsamplingRate
-  strategy.classWeights = Array(1.0, 1.0)
-  strategy
+      categoricalFeatures: Map[Int, Int],
+      numClasses: Int,
+      oldAlgo: OldAlgo.Algo,
+      oldImpurity: OldImpurity,
+      subsamplingRate: Double): OldStrategy = {
+    val strategy = OldStrategy.defaultStrategy(oldAlgo)
+    strategy.impurity = oldImpurity
+    strategy.checkpointInterval = getCheckpointInterval
+    strategy.maxBins = getMaxBins
+    strategy.maxDepth = getMaxDepth
+    strategy.maxMemoryInMB = getMaxMemoryInMB
+    strategy.minInfoGain = getMinInfoGain
+    strategy.minInstancesPerNode = getMinInstancesPerNode
+    strategy.useNodeIdCache = getCacheNodeIds
+    strategy.numClasses = numClasses
+    strategy.categoricalFeaturesInfo = categoricalFeatures
+    strategy.subsamplingRate = subsamplingRate
+    strategy.classWeights = Array(1.0, 1.0)
+    strategy
   }
 }
 

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

@@ -21,11 +21,10 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 
 /**
  * :: Experimental ::
- * Class for calculating the
- * [[http://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity Gini impurity]]
- * during binary classification.
+ * Class for calculating the Gini impurity with class weights using
+ * altered prior method during classification.
  */
-@Since("1.0.0")
+@Since("2.0.0")
 @Experimental
 object WeightedGini extends Impurity {
 
@@ -36,7 +35,7 @@ object WeightedGini extends Impurity {
    * @param weightedTotalCount sum of counts for all labels
    * @return information value, or 0 if totalCount = 0
    */
-  @Since("1.1.0")
+  @Since("2.0.0")
   @DeveloperApi
   override def calculate(weightedCounts: Array[Double], weightedTotalCount: Double): Double = {
     if (weightedTotalCount == 0) {
@@ -61,7 +60,7 @@ object WeightedGini extends Impurity {
    * @param sumSquares summation of squares of the labels
    * @return information value, or 0 if count = 0
    */
-  @Since("1.0.0")
+  @Since("2.0.0")
   @DeveloperApi
   override def calculate(count: Double, sum: Double, sumSquares: Double): Double =
     throw new UnsupportedOperationException("WeightedGini.calculate")
@@ -70,7 +69,7 @@ object WeightedGini extends Impurity {
    * Get this impurity instance.
    * This is useful for passing impurity parameters to a Strategy in Java.
    */
-  @Since("1.1.0")
+  @Since("2.0.0")
   def instance: this.type = this
 
 }
@@ -179,10 +178,10 @@ private[spark] class WeightedGiniCalculator(stats: Array[Double], classWeights:
       s"Two ImpurityCalculator instances cannot be added with different counts sizes." +
         s"  Sizes are ${stats.length} and ${other.stats.length}.")
     val otherCalculator = other.asInstanceOf[WeightedGiniCalculator]
+    val len = otherCalculator.stats.length
     var i = 0
-    val len = other.stats.length
     while (i < len) {
-      stats(i) += other.stats(i)
+      stats(i) += otherCalculator.stats(i)
       weightedStats(i) += otherCalculator.weightedStats(i)
       i += 1
     }
@@ -198,10 +197,10 @@ private[spark] class WeightedGiniCalculator(stats: Array[Double], classWeights:
       s"Two ImpurityCalculator instances cannot be subtracted with different counts sizes." +
         s"  Sizes are ${stats.length} and ${other.stats.length}.")
     val otherCalculator = other.asInstanceOf[WeightedGiniCalculator]
+    val len = otherCalculator.stats.length
     var i = 0
-    val len = other.stats.length
     while (i < len) {
-      stats(i) -= other.stats(i)
+      stats(i) -= otherCalculator.stats(i)
       weightedStats(i) -= otherCalculator.weightedStats(i)
       i += 1
     }

mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala

@@ -30,9 +30,6 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Row}
 
-
-
-
 class DecisionTreeClassifierSuite
   extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
 

mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala

@@ -17,8 +17,6 @@
 
 package org.apache.spark.ml.classification
 
-import scala.io.Source
-
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.feature.LabeledPoint
 import org.apache.spark.ml.linalg.{Vector, Vectors}
@@ -34,7 +32,6 @@ import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Row}
 
-
 /**
  * Test suite for [[RandomForestClassifier]].
  */
@@ -213,7 +210,7 @@ class RandomForestClassifierSuite
       assert(model.numClasses === model2.numClasses)
     }
 
-    val rf = new RandomForestClassifier().setNumTrees(2).setClassWeights(Array())
+    val rf = new RandomForestClassifier().setNumTrees(2)
     val rdd = TreeTests.getTreeReadWriteData(sc)
 
     val allParamSettings = TreeTests.allParamSettings ++ Map("impurity" -> "entropy")