[SPARK-29232][ML] Update the parameter maps of the DecisionTreeRegression/Classification Models

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

@@ -203,6 +203,7 @@ class GBTClassifier @Since("1.4.0") (
     } else {
       GradientBoostedTrees.run(trainDataset, boostingStrategy, $(seed), $(featureSubsetStrategy))
     }
+    baseLearners.foreach(copyValues(_))
 
     val numFeatures = baseLearners.head.numFeatures
     instr.logNumFeatures(numFeatures)

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

@@ -143,6 +143,7 @@ class RandomForestClassifier @Since("1.4.0") (
     val trees = RandomForest
       .run(instances, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed, Some(instr))
       .map(_.asInstanceOf[DecisionTreeClassificationModel])
+    trees.foreach(copyValues(_))
 
     val numFeatures = trees.head.numFeatures
     instr.logNumClasses(numClasses)

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

@@ -181,6 +181,7 @@ class GBTRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
       GradientBoostedTrees.run(trainDataset, boostingStrategy,
         $(seed), $(featureSubsetStrategy))
     }
+    baseLearners.foreach(copyValues(_))
 
     val numFeatures = baseLearners.head.numFeatures
     instr.logNumFeatures(numFeatures)

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

@@ -130,6 +130,7 @@ class RandomForestRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: S
     val trees = RandomForest
       .run(instances, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed, Some(instr))
       .map(_.asInstanceOf[DecisionTreeRegressionModel])
+    trees.foreach(copyValues(_))
 
     val numFeatures = trees.head.numFeatures
     instr.logNamedValue(Instrumentation.loggerTags.numFeatures, numFeatures)

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

@@ -456,6 +456,22 @@ class GBTClassifierSuite extends MLTest with DefaultReadWriteTest {
     }
   }
 
+  test("tree params") {
+    val categoricalFeatures = Map.empty[Int, Int]
+    val df: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 2)
+    val gbt = new GBTClassifier()
+      .setMaxDepth(2)
+      .setCheckpointInterval(5)
+      .setSeed(123)
+    val model = gbt.fit(df)
+
+    model.trees.foreach (i => {
+      assert(i.getMaxDepth === model.getMaxDepth)
+      assert(i.getCheckpointInterval === model.getCheckpointInterval)
+      assert(i.getSeed === model.getSeed)
+    })
+  }
+
   /////////////////////////////////////////////////////////////////////////////
   // Tests of model save/load
   /////////////////////////////////////////////////////////////////////////////

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

@@ -230,6 +230,26 @@ class RandomForestClassifierSuite extends MLTest with DefaultReadWriteTest {
       }
   }
 
+  test("tree params") {
+    val rdd = orderedLabeledPoints5_20
+    val rf = new RandomForestClassifier()
+      .setImpurity("entropy")
+      .setMaxDepth(3)
+      .setNumTrees(3)
+      .setSeed(123)
+    val categoricalFeatures = Map.empty[Int, Int]
+    val numClasses = 2
+
+    val df: DataFrame = TreeTests.setMetadata(rdd, categoricalFeatures, numClasses)
+    val model = rf.fit(df)
+
+    model.trees.foreach (i => {
+      assert(i.getMaxDepth === model.getMaxDepth)
+      assert(i.getSeed === model.getSeed)
+      assert(i.getImpurity === model.getImpurity)
+    })
+  }
+
   /////////////////////////////////////////////////////////////////////////////
   // Tests of model save/load
   /////////////////////////////////////////////////////////////////////////////

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

@@ -296,7 +296,21 @@ class GBTRegressorSuite extends MLTest with DefaultReadWriteTest {
     }
   }
 
-    /////////////////////////////////////////////////////////////////////////////
+  test("tree params") {
+    val gbt = new GBTRegressor()
+      .setMaxDepth(2)
+      .setCheckpointInterval(5)
+      .setSeed(123)
+    val model = gbt.fit(trainData.toDF)
+
+    model.trees.foreach (i => {
+      assert(i.getMaxDepth === model.getMaxDepth)
+      assert(i.getCheckpointInterval === model.getCheckpointInterval)
+      assert(i.getSeed === model.getSeed)
+    })
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
   // Tests of model save/load
   /////////////////////////////////////////////////////////////////////////////
 

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

@@ -139,6 +139,25 @@ class RandomForestRegressorSuite extends MLTest with DefaultReadWriteTest{
       }
   }
 
+  test("tree params") {
+    val rf = new RandomForestRegressor()
+      .setImpurity("variance")
+      .setMaxDepth(2)
+      .setMaxBins(10)
+      .setNumTrees(3)
+      .setSeed(123)
+
+    val df = orderedLabeledPoints50_1000.toDF()
+    val model = rf.fit(df)
+
+    model.trees.foreach (i => {
+      assert(i.getMaxDepth === model.getMaxDepth)
+      assert(i.getSeed === model.getSeed)
+      assert(i.getImpurity === model.getImpurity)
+      assert(i.getMaxBins === model.getMaxBins)
+    })
+  }
+
   /////////////////////////////////////////////////////////////////////////////
   // Tests of model save/load
   /////////////////////////////////////////////////////////////////////////////