[SPARK-30630][ML][2.4] Deprecate numTrees in GBT in 2.4.5

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

@@ -319,7 +319,12 @@ class GBTClassificationModel private[ml](
     }
   }
 
-  /** Number of trees in ensemble */
+  /**
+   * Number of trees in ensemble
+   *
+   * @deprecated  Use [[getNumTrees]] instead. This method will be removed in 3.0.0.
+   */
+  @deprecated("Use getNumTrees instead. This method will be removed in 3.0.0.", "2.4.5")
   val numTrees: Int = trees.length
 
   @Since("1.4.0")
@@ -330,7 +335,7 @@ class GBTClassificationModel private[ml](
 
   @Since("1.4.0")
   override def toString: String = {
-    s"GBTClassificationModel (uid=$uid) with $numTrees trees"
+    s"GBTClassificationModel (uid=$uid) with $getNumTrees trees"
   }
 
   /**
@@ -349,7 +354,7 @@ 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

@@ -255,10 +255,15 @@ 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 */
+  /**
+   * Number of trees in ensemble
+   *
+   * @deprecated  Use [[getNumTrees]] instead. This method will be removed in 3.0.0.
+   */
+  @deprecated("Use getNumTrees instead. This method will be removed in 3.0.0.", "2.4.5")
   val numTrees: Int = trees.length
 
   @Since("1.4.0")
@@ -269,7 +274,7 @@ class GBTRegressionModel private[ml](
 
   @Since("1.4.0")
   override def toString: String = {
-    s"GBTRegressionModel (uid=$uid) with $numTrees trees"
+    s"GBTRegressionModel (uid=$uid) with $getNumTrees trees"
   }
 
   /**

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

@@ -178,7 +178,8 @@ 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
@@ -410,9 +411,9 @@ 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(x => new LabeledPoint(2 * x.label - 1, x.features))
@@ -428,10 +429,10 @@ 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

@@ -249,9 +249,9 @@ 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.computeError(validationData,
         modelWithoutValidation.trees, modelWithoutValidation.treeWeights,
@@ -267,10 +267,10 @@ 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
       }