[SPARK-12301][ML] Made all tree and ensemble classes not final

## What changes were proposed in this pull request?

There have been continuing requests (e.g., SPARK-7131) for allowing users to extend and modify MLlib models and algorithms.

This PR makes tree and ensemble classes, Node types, and Split types in spark.ml no longer final.  This matches most other spark.ml algorithms.

Constructors for models are still private since we may need to refactor how stats are maintained in tree nodes.

## How was this patch tested?

Existing unit tests

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #12711 from jkbradley/final-trees.

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

@@ -44,7 +44,7 @@ import org.apache.spark.sql.Dataset
  */
 @Since("1.4.0")
 @Experimental
-final class DecisionTreeClassifier @Since("1.4.0") (
+class DecisionTreeClassifier @Since("1.4.0") (
     @Since("1.4.0") override val uid: String)
   extends ProbabilisticClassifier[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel]
   with DecisionTreeClassifierParams with DefaultParamsWritable {
@@ -138,7 +138,7 @@ object DecisionTreeClassifier extends DefaultParamsReadable[DecisionTreeClassifi
  */
 @Since("1.4.0")
 @Experimental
-final class DecisionTreeClassificationModel private[ml] (
+class DecisionTreeClassificationModel private[ml] (
     @Since("1.4.0")override val uid: String,
     @Since("1.4.0")override val rootNode: Node,
     @Since("1.6.0")override val numFeatures: Int,

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

@@ -57,7 +57,7 @@ import org.apache.spark.sql.functions._
  */
 @Since("1.4.0")
 @Experimental
-final class GBTClassifier @Since("1.4.0") (
+class GBTClassifier @Since("1.4.0") (
     @Since("1.4.0") override val uid: String)
   extends Predictor[Vector, GBTClassifier, GBTClassificationModel]
   with GBTClassifierParams with DefaultParamsWritable with Logging {
@@ -170,7 +170,7 @@ object GBTClassifier extends DefaultParamsReadable[GBTClassifier] {
  */
 @Since("1.6.0")
 @Experimental
-final class GBTClassificationModel private[ml](
+class GBTClassificationModel private[ml](
     @Since("1.6.0") override val uid: String,
     private val _trees: Array[DecisionTreeRegressionModel],
     private val _treeWeights: Array[Double],

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

@@ -44,7 +44,7 @@ import org.apache.spark.sql.functions._
  */
 @Since("1.4.0")
 @Experimental
-final class RandomForestClassifier @Since("1.4.0") (
+class RandomForestClassifier @Since("1.4.0") (
     @Since("1.4.0") override val uid: String)
   extends ProbabilisticClassifier[Vector, RandomForestClassifier, RandomForestClassificationModel]
   with RandomForestClassifierParams with DefaultParamsWritable {
@@ -149,7 +149,7 @@ object RandomForestClassifier extends DefaultParamsReadable[RandomForestClassifi
  */
 @Since("1.4.0")
 @Experimental
-final class RandomForestClassificationModel private[ml] (
+class RandomForestClassificationModel private[ml] (
     @Since("1.5.0") override val uid: String,
     private val _trees: Array[DecisionTreeClassificationModel],
     @Since("1.6.0") override val numFeatures: Int,

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

@@ -45,7 +45,7 @@ import org.apache.spark.sql.functions._
  */
 @Since("1.4.0")
 @Experimental
-final class DecisionTreeRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
+class DecisionTreeRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
   extends Predictor[Vector, DecisionTreeRegressor, DecisionTreeRegressionModel]
   with DecisionTreeRegressorParams with DefaultParamsWritable {
 
@@ -129,7 +129,7 @@ object DecisionTreeRegressor extends DefaultParamsReadable[DecisionTreeRegressor
  */
 @Since("1.4.0")
 @Experimental
-final class DecisionTreeRegressionModel private[ml] (
+class DecisionTreeRegressionModel private[ml] (
     override val uid: String,
     override val rootNode: Node,
     override val numFeatures: Int)

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

@@ -57,7 +57,7 @@ import org.apache.spark.sql.functions._
  */
 @Since("1.4.0")
 @Experimental
-final class GBTRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
+class GBTRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
   extends Predictor[Vector, GBTRegressor, GBTRegressionModel]
   with GBTRegressorParams with DefaultParamsWritable with Logging {
 
@@ -157,7 +157,7 @@ object GBTRegressor extends DefaultParamsReadable[GBTRegressor] {
  */
 @Since("1.4.0")
 @Experimental
-final class GBTRegressionModel private[ml](
+class GBTRegressionModel private[ml](
     override val uid: String,
     private val _trees: Array[DecisionTreeRegressionModel],
     private val _treeWeights: Array[Double],

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

@@ -43,7 +43,7 @@ import org.apache.spark.sql.functions._
  */
 @Since("1.4.0")
 @Experimental
-final class RandomForestRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
+class RandomForestRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
   extends Predictor[Vector, RandomForestRegressor, RandomForestRegressionModel]
   with RandomForestRegressorParams with DefaultParamsWritable {
 
@@ -137,7 +137,7 @@ object RandomForestRegressor extends DefaultParamsReadable[RandomForestRegressor
  */
 @Since("1.4.0")
 @Experimental
-final class RandomForestRegressionModel private[ml] (
+class RandomForestRegressionModel private[ml] (
     override val uid: String,
     private val _trees: Array[DecisionTreeRegressionModel],
     override val numFeatures: Int)

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

@@ -115,7 +115,7 @@ private[ml] object Node {
  * @param impurity  Impurity measure at this node (for training data)
  */
 @DeveloperApi
-final class LeafNode private[ml] (
+class LeafNode private[ml] (
     override val prediction: Double,
     override val impurity: Double,
     override private[ml] val impurityStats: ImpurityCalculator) extends Node {
@@ -158,7 +158,7 @@ final class LeafNode private[ml] (
  * @param split  Information about the test used to split to the left or right child.
  */
 @DeveloperApi
-final class InternalNode private[ml] (
+class InternalNode private[ml] (
     override val prediction: Double,
     override val impurity: Double,
     val gain: Double,

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

@@ -75,7 +75,7 @@ private[tree] object Split {
  * @param numCategories  Number of categories for this feature.
  */
 @DeveloperApi
-final class CategoricalSplit private[ml] (
+class CategoricalSplit private[ml] (
     override val featureIndex: Int,
     _leftCategories: Array[Double],
     @Since("2.0.0") val numCategories: Int)
@@ -160,7 +160,7 @@ final class CategoricalSplit private[ml] (
  *                    Otherwise, it goes right.
  */
 @DeveloperApi
-final class ContinuousSplit private[ml] (override val featureIndex: Int, val threshold: Double)
+class ContinuousSplit private[ml] (override val featureIndex: Int, val threshold: Double)
   extends Split {
 
   override private[ml] def shouldGoLeft(features: Vector): Boolean = {