[SPARK-30630][ML] Remove numTrees in GBT in 3.0.0

### What changes were proposed in this pull request?
Remove ```numTrees``` in GBT in 3.0.0.

### Why are the changes needed?
Currently, GBT has
```
  /**
   * Number of trees in ensemble
   */
  Since("2.0.0")
  val getNumTrees: Int = trees.length
```
and
```
  /** Number of trees in ensemble */
  val numTrees: Int = trees.length
```
I think we should remove one of them. We deprecated it in 2.4.5 via #27352.

### Does this PR introduce any user-facing change?
Yes, remove ```numTrees``` in GBT in 3.0.0

### How was this patch tested?
existing tests

Closes #27330 from huaxingao/spark-numTrees.

Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>

Author: huaxingao

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

@@ -342,9 +342,6 @@ class GBTClassificationModel private[ml](
     }
   }
 
-  /** Number of trees in ensemble */
-  val numTrees: Int = trees.length
-
   @Since("1.4.0")
   override def copy(extra: ParamMap): GBTClassificationModel = {
     copyValues(new GBTClassificationModel(uid, _trees, _treeWeights, numFeatures, numClasses),
@@ -353,7 +350,7 @@ class GBTClassificationModel private[ml](
 
   @Since("1.4.0")
   override def toString: String = {
-    s"GBTClassificationModel: uid = $uid, numTrees=$numTrees, numClasses=$numClasses, " +
+    s"GBTClassificationModel: uid = $uid, numTrees=$getNumTrees, numClasses=$numClasses, " +
       s"numFeatures=$numFeatures"
   }
 
@@ -374,7 +371,7 @@ class GBTClassificationModel private[ml](
   /** Raw prediction for the positive class. */
   private def margin(features: Vector): Double = {
     val treePredictions = _trees.map(_.rootNode.predictImpl(features).prediction)
-    blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1)
+    blas.ddot(getNumTrees, treePredictions, 1, _treeWeights, 1)
   }
 
   /** (private[ml]) Convert to a model in the old API */

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

@@ -299,12 +299,9 @@ class GBTRegressionModel private[ml](
     // TODO: When we add a generic Boosting class, handle transform there?  SPARK-7129
     // Classifies by thresholding sum of weighted tree predictions
     val treePredictions = _trees.map(_.rootNode.predictImpl(features).prediction)
-    blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1)
+    blas.ddot(getNumTrees, treePredictions, 1, _treeWeights, 1)
   }
 
-  /** Number of trees in ensemble */
-  val numTrees: Int = trees.length
-
   @Since("1.4.0")
   override def copy(extra: ParamMap): GBTRegressionModel = {
     copyValues(new GBTRegressionModel(uid, _trees, _treeWeights, numFeatures),
@@ -313,7 +310,7 @@ class GBTRegressionModel private[ml](
 
   @Since("1.4.0")
   override def toString: String = {
-    s"GBTRegressionModel: uid=$uid, numTrees=$numTrees, numFeatures=$numFeatures"
+    s"GBTRegressionModel: uid=$uid, numTrees=$getNumTrees, numFeatures=$numFeatures"
   }
 
   /**

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

@@ -179,7 +179,8 @@ class GBTClassifierSuite extends MLTest with DefaultReadWriteTest {
         assert(raw.size === 2)
         // check that raw prediction is tree predictions dot tree weights
         val treePredictions = gbtModel.trees.map(_.rootNode.predictImpl(features).prediction)
-        val prediction = blas.ddot(gbtModel.numTrees, treePredictions, 1, gbtModel.treeWeights, 1)
+        val prediction = blas.ddot(gbtModel.getNumTrees, treePredictions, 1,
+          gbtModel.treeWeights, 1)
         assert(raw ~== Vectors.dense(-prediction, prediction) relTol eps)
 
         // Compare rawPrediction with probability
@@ -436,9 +437,9 @@ class GBTClassifierSuite extends MLTest with DefaultReadWriteTest {
       gbt.setValidationIndicatorCol(validationIndicatorCol)
       val modelWithValidation = gbt.fit(trainDF.union(validationDF))
 
-      assert(modelWithoutValidation.numTrees === numIter)
+      assert(modelWithoutValidation.getNumTrees === numIter)
       // early stop
-      assert(modelWithValidation.numTrees < numIter)
+      assert(modelWithValidation.getNumTrees < numIter)
 
       val (errorWithoutValidation, errorWithValidation) = {
         val remappedRdd = validationData.map {
@@ -457,10 +458,10 @@ class GBTClassifierSuite extends MLTest with DefaultReadWriteTest {
           modelWithoutValidation.treeWeights, modelWithoutValidation.getOldLossType,
           OldAlgo.Classification)
       assert(evaluationArray.length === numIter)
-      assert(evaluationArray(modelWithValidation.numTrees) >
-        evaluationArray(modelWithValidation.numTrees - 1))
+      assert(evaluationArray(modelWithValidation.getNumTrees) >
+        evaluationArray(modelWithValidation.getNumTrees - 1))
       var i = 1
-      while (i < modelWithValidation.numTrees) {
+      while (i < modelWithValidation.getNumTrees) {
         assert(evaluationArray(i) <= evaluationArray(i - 1))
         i += 1
       }

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

@@ -274,9 +274,9 @@ class GBTRegressorSuite extends MLTest with DefaultReadWriteTest {
       gbt.setValidationIndicatorCol(validationIndicatorCol)
       val modelWithValidation = gbt.fit(trainDF.union(validationDF))
 
-      assert(modelWithoutValidation.numTrees === numIter)
+      assert(modelWithoutValidation.getNumTrees === numIter)
       // early stop
-      assert(modelWithValidation.numTrees < numIter)
+      assert(modelWithValidation.getNumTrees < numIter)
 
       val errorWithoutValidation = GradientBoostedTrees.computeWeightedError(
         validationData.map(_.toInstance),
@@ -294,10 +294,10 @@ class GBTRegressorSuite extends MLTest with DefaultReadWriteTest {
           modelWithoutValidation.treeWeights, modelWithoutValidation.getOldLossType,
           OldAlgo.Regression)
       assert(evaluationArray.length === numIter)
-      assert(evaluationArray(modelWithValidation.numTrees) >
-        evaluationArray(modelWithValidation.numTrees - 1))
+      assert(evaluationArray(modelWithValidation.getNumTrees) >
+        evaluationArray(modelWithValidation.getNumTrees - 1))
       var i = 1
-      while (i < modelWithValidation.numTrees) {
+      while (i < modelWithValidation.getNumTrees) {
         assert(evaluationArray(i) <= evaluationArray(i - 1))
         i += 1
       }

project/MimaExcludes.scala

@@ -344,6 +344,10 @@ object MimaExcludes {
     ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel.layers"),
     ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel.this"),
 
+    // [SPARK-30630][ML] Remove numTrees in GBT
+    ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.ml.classification.GBTClassificationModel.numTrees"),
+    ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.ml.regression.GBTRegressionModel.numTrees"),
+
     // Data Source V2 API changes
     (problem: Problem) => problem match {
       case MissingClassProblem(cls) =>