first version that pass all run_test, by adding redundant constructor…

… and reverting getOldStrategy to old versions

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

@@ -122,7 +122,7 @@ class DecisionTreeClassifier @Since("1.4.0") (
       categoricalFeatures: Map[Int, Int],
       numClasses: Int): OldStrategy = {
     super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity,
-      subsamplingRate = 1.0, getClassWeights)
+      subsamplingRate = 1.0)
   }
 
   @Since("1.4.1")

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

@@ -108,7 +108,7 @@ class RandomForestClassifier @Since("1.4.0") (
     val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, numClasses)
     val strategy =
       super.getOldStrategy(categoricalFeatures, numClasses,
-        OldAlgo.Classification, getOldImpurity, getClassWeights)
+        OldAlgo.Classification, getOldImpurity)
 
     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,
-      subsamplingRate = 1.0, classWeights = Array())
+      subsamplingRate = 1.0)
   }
 
   @Since("1.4.0")

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

@@ -99,7 +99,7 @@ class RandomForestRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: S
     val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
     val strategy =
       super.getOldStrategy(categoricalFeatures, numClasses = 0,
-        OldAlgo.Regression, getOldImpurity, Array())
+        OldAlgo.Regression, getOldImpurity)
 
     val instr = Instrumentation.create(this, oldDataset)
     instr.logParams(params: _*)

mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala

@@ -84,7 +84,7 @@ class Strategy @Since("1.3.0") (
     @Since("1.2.0") @BeanProperty var subsamplingRate: Double = 1,
     @Since("1.2.0") @BeanProperty var useNodeIdCache: Boolean = false,
     @Since("1.2.0") @BeanProperty var checkpointInterval: Int = 10,
-    @Since("2.0.0") var classWeights: Array[Double] = Array(1, 1))
+    @Since("2.0.0") @BeanProperty var classWeights: Array[Double] = Array(1.0, 1.0))
   extends Serializable {
 
   /**
@@ -101,6 +101,29 @@ class Strategy @Since("1.3.0") (
     isMulticlassClassification && (categoricalFeaturesInfo.size > 0)
   }
 
+  /**
+   *  Make the class compatible with previous versions
+   */
+  @Since("2.0.0")
+  def this(
+    algo: Algo,
+    impurity: Impurity,
+    maxDepth: Int,
+    numClasses: Int,
+    maxBins: Int,
+    quantileCalculationStrategy: QuantileStrategy,
+    categoricalFeaturesInfo: Map[Int, Int],
+    minInstancesPerNode: Int,
+    minInfoGain: Double,
+    maxMemoryInMB: Int,
+    subsamplingRate: Double,
+    useNodeIdCache: Boolean,
+    checkpointInterval: Int) {
+    this(algo, impurity, maxDepth, numClasses, maxBins, quantileCalculationStrategy,
+      categoricalFeaturesInfo, minInstancesPerNode, minInfoGain, maxMemoryInMB,
+      subsamplingRate, useNodeIdCache, checkpointInterval, Array())
+  }
+
   /**
    * Java-friendly constructor for [[org.apache.spark.mllib.tree.configuration.Strategy]]
    */

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

@@ -238,7 +238,7 @@ private object RandomForestClassifierSuite extends SparkFunSuite {
     val numFeatures = data.first().features.size
     val oldStrategy =
       rf.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification,
-        rf.getOldImpurity, rf.getClassWeights)
+        rf.getOldImpurity)
     val oldModel = OldRandomForest.trainClassifier(
       data.map(OldLabeledPoint.fromML), oldStrategy, rf.getNumTrees, rf.getFeatureSubsetStrategy,
       rf.getSeed.toInt)

mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala

@@ -141,7 +141,7 @@ private object RandomForestRegressorSuite extends SparkFunSuite {
     val numFeatures = data.first().features.size
     val oldStrategy =
       rf.getOldStrategy(categoricalFeatures, numClasses = 0,
-        OldAlgo.Regression, rf.getOldImpurity, classWeights = Array())
+        OldAlgo.Regression, rf.getOldImpurity)
     val oldModel = OldRandomForest.trainRegressor(data.map(OldLabeledPoint.fromML), oldStrategy,
       rf.getNumTrees, rf.getFeatureSubsetStrategy, rf.getSeed.toInt)
     val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0)

scalastyle-config.xml

@@ -94,7 +94,7 @@ This file is divided into 3 sections:
   </check>
 
   <check level="error" class="org.scalastyle.scalariform.ParameterNumberChecker" enabled="true">
-    <parameters><parameter name="maxParameters"><![CDATA[10]]></parameter></parameters>
+    <parameters><parameter name="maxParameters"><![CDATA[15]]></parameter></parameters>
   </check>
 
   <check level="error" class="org.scalastyle.scalariform.NoFinalizeChecker" enabled="true"></check>