From 10632436e107d9bec13f22bf850340a0deab82a8 Mon Sep 17 00:00:00 2001
From: "Ehsan M.Kermani" <ehsanmo1367@gmail.com>
Date: Fri, 11 Sep 2015 16:49:02 -0700
Subject: [PATCH 1/5] Since annotation for ml.regression

---
 .../ml/regression/DecisionTreeRegressor.scala | 26 +++++-----
 .../spark/ml/regression/GBTRegressor.scala    | 52 +++++++++++--------
 .../ml/regression/IsotonicRegression.scala    | 21 +++++---
 .../ml/regression/LinearRegression.scala      | 15 ++++--
 .../ml/regression/RandomForestRegressor.scala | 34 ++++++------
 5 files changed, 86 insertions(+), 62 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
index a2bcd67401d0..77d47e5458e9 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.regression
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{DecisionTreeModel, DecisionTreeParams, Node, TreeRegressorParams}
@@ -36,30 +36,31 @@ import org.apache.spark.sql.DataFrame
  * for regression.
  * It supports both continuous and categorical features.
  */
+@Since("1.4.0") 
 @Experimental
-final class DecisionTreeRegressor(override val uid: String)
+final class DecisionTreeRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
   extends Predictor[Vector, DecisionTreeRegressor, DecisionTreeRegressionModel]
   with DecisionTreeParams with TreeRegressorParams {
-
+  @Since("1.4.0")
   def this() = this(Identifiable.randomUID("dtr"))
 
   // Override parameter setters from parent trait for Java API compatibility.
-
+  @Since("1.4.0")
   override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value)
-
+  @Since("1.4.0")
   override def setMaxBins(value: Int): this.type = super.setMaxBins(value)
-
+  @Since("1.4.0")
   override def setMinInstancesPerNode(value: Int): this.type =
     super.setMinInstancesPerNode(value)
-
+  @Since("1.4.0")
   override def setMinInfoGain(value: Double): this.type = super.setMinInfoGain(value)
-
+  @Since("1.4.0")
   override def setMaxMemoryInMB(value: Int): this.type = super.setMaxMemoryInMB(value)
-
+  @Since("1.4.0")  
   override def setCacheNodeIds(value: Boolean): this.type = super.setCacheNodeIds(value)
-
+  @Since("1.4.0")
   override def setCheckpointInterval(value: Int): this.type = super.setCheckpointInterval(value)
-
+  @Since("1.4.0")
   override def setImpurity(value: String): this.type = super.setImpurity(value)
 
   override protected def train(dataset: DataFrame): DecisionTreeRegressionModel = {
@@ -77,10 +78,11 @@ final class DecisionTreeRegressor(override val uid: String)
     super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, getOldImpurity,
       subsamplingRate = 1.0)
   }
-
+  @Since("1.4.0")
   override def copy(extra: ParamMap): DecisionTreeRegressor = defaultCopy(extra)
 }
 
+@Since("1.4.0")
 @Experimental
 object DecisionTreeRegressor {
   /** Accessor for supported impurities: variance */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index b66e61f37dd5..662ada9f3cde 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.regression
 import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.{Param, ParamMap}
 import org.apache.spark.ml.tree.{DecisionTreeModel, GBTParams, TreeEnsembleModel, TreeRegressorParams}
@@ -42,54 +42,56 @@ import org.apache.spark.sql.types.DoubleType
  * learning algorithm for regression.
  * It supports both continuous and categorical features.
  */
+@Since("1.4.0")
 @Experimental
-final class GBTRegressor(override val uid: String)
+final class GBTRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
   extends Predictor[Vector, GBTRegressor, GBTRegressionModel]
   with GBTParams with TreeRegressorParams with Logging {
-
+  @Since("1.4.0")
   def this() = this(Identifiable.randomUID("gbtr"))
 
   // Override parameter setters from parent trait for Java API compatibility.
 
   // Parameters from TreeRegressorParams:
-
+  @Since("1.4.0")
   override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value)
-
+  @Since("1.4.0")
   override def setMaxBins(value: Int): this.type = super.setMaxBins(value)
-
+  @Since("1.4.0")
   override def setMinInstancesPerNode(value: Int): this.type =
     super.setMinInstancesPerNode(value)
-
+  @Since("1.4.0")
   override def setMinInfoGain(value: Double): this.type = super.setMinInfoGain(value)
-
+  @Since("1.4.0")
   override def setMaxMemoryInMB(value: Int): this.type = super.setMaxMemoryInMB(value)
-
+  @Since("1.4.0")
   override def setCacheNodeIds(value: Boolean): this.type = super.setCacheNodeIds(value)
-
+  @Since("1.4.0")
   override def setCheckpointInterval(value: Int): this.type = super.setCheckpointInterval(value)
 
   /**
    * The impurity setting is ignored for GBT models.
    * Individual trees are built using impurity "Variance."
    */
+  @Since("1.4.0")
   override def setImpurity(value: String): this.type = {
     logWarning("GBTRegressor.setImpurity should NOT be used")
     this
   }
 
   // Parameters from TreeEnsembleParams:
-
+  @Since("1.4.0")
   override def setSubsamplingRate(value: Double): this.type = super.setSubsamplingRate(value)
-
+  @Since("1.4.0")
   override def setSeed(value: Long): this.type = {
     logWarning("The 'seed' parameter is currently ignored by Gradient Boosting.")
     super.setSeed(value)
   }
 
   // Parameters from GBTParams:
-
+  @Since("1.4.0")
   override def setMaxIter(value: Int): this.type = super.setMaxIter(value)
-
+  @Since("1.4.0")
   override def setStepSize(value: Double): this.type = super.setStepSize(value)
 
   // Parameters for GBTRegressor:
@@ -108,9 +110,11 @@ final class GBTRegressor(override val uid: String)
   setDefault(lossType -> "squared")
 
   /** @group setParam */
+  @Since("1.4.0")
   def setLossType(value: String): this.type = set(lossType, value)
 
   /** @group getParam */
+  @Since("1.4.0")
   def getLossType: String = $(lossType).toLowerCase
 
   /** (private[ml]) Convert new loss to old loss. */
@@ -133,10 +137,11 @@ final class GBTRegressor(override val uid: String)
     val oldModel = oldGBT.run(oldDataset)
     GBTRegressionModel.fromOld(oldModel, this, categoricalFeatures)
   }
-
+  @Since("1.4.0")
   override def copy(extra: ParamMap): GBTRegressor = defaultCopy(extra)
 }
 
+@Since("1.4.0")
 @Experimental
 object GBTRegressor {
   // The losses below should be lowercase.
@@ -153,20 +158,21 @@ object GBTRegressor {
  * @param _trees  Decision trees in the ensemble.
  * @param _treeWeights  Weights for the decision trees in the ensemble.
  */
+@Since("1.4.0")
 @Experimental
-final class GBTRegressionModel(
-    override val uid: String,
-    private val _trees: Array[DecisionTreeRegressionModel],
-    private val _treeWeights: Array[Double])
+final class GBTRegressionModel @Since("1.4.0") (
+                                                 @Since("1.4.0") override val uid: String,
+                                                 private val _trees: Array[DecisionTreeRegressionModel],
+                                                 private val _treeWeights: Array[Double])
   extends PredictionModel[Vector, GBTRegressionModel]
   with TreeEnsembleModel with Serializable {
 
   require(numTrees > 0, "GBTRegressionModel requires at least 1 tree.")
   require(_trees.length == _treeWeights.length, "GBTRegressionModel given trees, treeWeights of" +
     s" non-matching lengths (${_trees.length}, ${_treeWeights.length}, respectively).")
-
+  @Since("1.4.0")
   override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
-
+  @Since("1.4.0")
   override def treeWeights: Array[Double] = _treeWeights
 
   override protected def transformImpl(dataset: DataFrame): DataFrame = {
@@ -183,11 +189,11 @@ final class GBTRegressionModel(
     val treePredictions = _trees.map(_.rootNode.predictImpl(features).prediction)
     blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1)
   }
-
+  @Since("1.4.0")
   override def copy(extra: ParamMap): GBTRegressionModel = {
     copyValues(new GBTRegressionModel(uid, _trees, _treeWeights), extra).setParent(parent)
   }
-
+  @Since("1.4.0")
   override def toString: String = {
     s"GBTRegressionModel with $numTrees trees"
   }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
index 2ff500f291ab..4236c6d8534d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.ml.regression
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol, HasPredictionCol, HasWeightCol}
@@ -124,32 +124,39 @@ private[regression] trait IsotonicRegressionBase extends Params with HasFeatures
  *
  * Uses [[org.apache.spark.mllib.regression.IsotonicRegression]].
  */
+@Since("1.5.0")
 @Experimental
-class IsotonicRegression(override val uid: String) extends Estimator[IsotonicRegressionModel]
-  with IsotonicRegressionBase {
-
+class IsotonicRegression @Since("1.5.0") (@Since("1.5.0") override val uid: String)
+  extends Estimator[IsotonicRegressionModel] with IsotonicRegressionBase {
+  @Since("1.5.0")
   def this() = this(Identifiable.randomUID("isoReg"))
 
   /** @group setParam */
+  @Since("1.5.0")
   def setLabelCol(value: String): this.type = set(labelCol, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setFeaturesCol(value: String): this.type = set(featuresCol, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setPredictionCol(value: String): this.type = set(predictionCol, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setIsotonic(value: Boolean): this.type = set(isotonic, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setWeightCol(value: String): this.type = set(weightCol, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setFeatureIndex(value: Int): this.type = set(featureIndex, value)
-
+  @Since("1.5.0")
   override def copy(extra: ParamMap): IsotonicRegression = defaultCopy(extra)
-
+  @Since("1.5.0")
   override def fit(dataset: DataFrame): IsotonicRegressionModel = {
     validateAndTransformSchema(dataset.schema, fitting = true)
     // Extract columns from data.  If dataset is persisted, do not persist oldDataset.
@@ -162,7 +169,7 @@ class IsotonicRegression(override val uid: String) extends Estimator[IsotonicReg
 
     copyValues(new IsotonicRegressionModel(uid, oldModel).setParent(this))
   }
-
+  @Since("1.5.0")
   override def transformSchema(schema: StructType): StructType = {
     validateAndTransformSchema(schema, fitting = true)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index e4602d36ccc8..3c1e17e341ee 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -23,7 +23,7 @@ import breeze.linalg.{DenseVector => BDV, norm => brzNorm}
 import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN}
 
 import org.apache.spark.{Logging, SparkException}
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.PredictorParams
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.param.shared._
@@ -61,11 +61,12 @@ private[regression] trait LinearRegressionParams extends PredictorParams
  *  - L1 (Lasso)
  *  - L2 + L1 (elastic net)
  */
+@Since("1.3.0")
 @Experimental
-class LinearRegression(override val uid: String)
+class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String)
   extends Regressor[Vector, LinearRegression, LinearRegressionModel]
   with LinearRegressionParams with Logging {
-
+  @Since("1.4.0")
   def this() = this(Identifiable.randomUID("linReg"))
 
   /**
@@ -73,6 +74,7 @@ class LinearRegression(override val uid: String)
    * Default is 0.0.
    * @group setParam
    */
+  @Since("1.3.0")
   def setRegParam(value: Double): this.type = set(regParam, value)
   setDefault(regParam -> 0.0)
 
@@ -81,6 +83,7 @@ class LinearRegression(override val uid: String)
    * Default is true.
    * @group setParam
    */
+  @Since("1.5.0")
   def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
   setDefault(fitIntercept -> true)
 
@@ -93,6 +96,7 @@ class LinearRegression(override val uid: String)
    * Default is true.
    * @group setParam
    */
+  @Since("1.5.0")
   def setStandardization(value: Boolean): this.type = set(standardization, value)
   setDefault(standardization -> true)
 
@@ -103,6 +107,7 @@ class LinearRegression(override val uid: String)
    * Default is 0.0 which is an L2 penalty.
    * @group setParam
    */
+  @Since("1.4.0")
   def setElasticNetParam(value: Double): this.type = set(elasticNetParam, value)
   setDefault(elasticNetParam -> 0.0)
 
@@ -111,6 +116,7 @@ class LinearRegression(override val uid: String)
    * Default is 100.
    * @group setParam
    */
+  @Since("1.3.0")
   def setMaxIter(value: Int): this.type = set(maxIter, value)
   setDefault(maxIter -> 100)
 
@@ -120,6 +126,7 @@ class LinearRegression(override val uid: String)
    * Default is 1E-6.
    * @group setParam
    */
+  @Since("1.4.0")
   def setTol(value: Double): this.type = set(tol, value)
   setDefault(tol -> 1E-6)
 
@@ -254,7 +261,7 @@ class LinearRegression(override val uid: String)
       objectiveHistory)
     model.setSummary(trainingSummary)
   }
-
+  @Since("1.3.0")
   override def copy(extra: ParamMap): LinearRegression = defaultCopy(extra)
 }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index 2f36da371f57..a546e034aa79 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.regression
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{DecisionTreeModel, RandomForestParams, TreeEnsembleModel, TreeRegressorParams}
@@ -37,44 +37,45 @@ import org.apache.spark.sql.functions._
  * [[http://en.wikipedia.org/wiki/Random_forest  Random Forest]] learning algorithm for regression.
  * It supports both continuous and categorical features.
  */
+@Since("1.4.0")
 @Experimental
-final class RandomForestRegressor(override val uid: String)
+final class RandomForestRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
   extends Predictor[Vector, RandomForestRegressor, RandomForestRegressionModel]
   with RandomForestParams with TreeRegressorParams {
-
+  @Since("1.4.0")
   def this() = this(Identifiable.randomUID("rfr"))
 
   // Override parameter setters from parent trait for Java API compatibility.
 
   // Parameters from TreeRegressorParams:
-
+  @Since("1.4.0")
   override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value)
-
+  @Since("1.4.0")
   override def setMaxBins(value: Int): this.type = super.setMaxBins(value)
-
+  @Since("1.4.0")
   override def setMinInstancesPerNode(value: Int): this.type =
     super.setMinInstancesPerNode(value)
-
+  @Since("1.4.0")
   override def setMinInfoGain(value: Double): this.type = super.setMinInfoGain(value)
-
+  @Since("1.4.0")
   override def setMaxMemoryInMB(value: Int): this.type = super.setMaxMemoryInMB(value)
-
+  @Since("1.4.0")
   override def setCacheNodeIds(value: Boolean): this.type = super.setCacheNodeIds(value)
-
+  @Since("1.4.0")
   override def setCheckpointInterval(value: Int): this.type = super.setCheckpointInterval(value)
-
+  @Since("1.4.0")
   override def setImpurity(value: String): this.type = super.setImpurity(value)
 
   // Parameters from TreeEnsembleParams:
-
+  @Since("1.4.0")
   override def setSubsamplingRate(value: Double): this.type = super.setSubsamplingRate(value)
-
+  @Since("1.4.0")
   override def setSeed(value: Long): this.type = super.setSeed(value)
 
   // Parameters from RandomForestParams:
-
+  @Since("1.4.0")
   override def setNumTrees(value: Int): this.type = super.setNumTrees(value)
-
+  @Since("1.4.0")
   override def setFeatureSubsetStrategy(value: String): this.type =
     super.setFeatureSubsetStrategy(value)
 
@@ -90,10 +91,11 @@ final class RandomForestRegressor(override val uid: String)
     val numFeatures = oldDataset.first().features.size
     new RandomForestRegressionModel(trees, numFeatures)
   }
-
+  @Since("1.4.0")
   override def copy(extra: ParamMap): RandomForestRegressor = defaultCopy(extra)
 }
 
+@Since("1.4.0")
 @Experimental
 object RandomForestRegressor {
   /** Accessor for supported impurity settings: variance */

From 40dc4f24d356ce246fbf65698578c3d688a790dc Mon Sep 17 00:00:00 2001
From: "Ehsan M.Kermani" <ehsanmo1367@gmail.com>
Date: Fri, 11 Sep 2015 17:57:03 -0700
Subject: [PATCH 2/5] Since annotation for ml.tuning

---
 .../spark/ml/tuning/CrossValidator.scala      | 20 ++++++++++++-------
 .../spark/ml/tuning/ParamGridBuilder.scala    | 12 ++++++++++-
 .../ml/tuning/TrainValidationSplit.scala      | 20 ++++++++++++-------
 3 files changed, 37 insertions(+), 15 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
index 0679bfd0f3ff..67e2a6683142 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.tuning
 import com.github.fommil.netlib.F2jBLAS
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml._
 import org.apache.spark.ml.evaluation.Evaluator
 import org.apache.spark.ml.param._
@@ -51,26 +51,32 @@ private[ml] trait CrossValidatorParams extends ValidatorParams {
  * :: Experimental ::
  * K-fold cross validation.
  */
+@Since("1.2.0")
 @Experimental
-class CrossValidator(override val uid: String) extends Estimator[CrossValidatorModel]
+class CrossValidator @Since("1.2.0") (@Since("1.2.0") override val uid: String)
+  extends Estimator[CrossValidatorModel]
   with CrossValidatorParams with Logging {
-
+  @Since("1.4.0")
   def this() = this(Identifiable.randomUID("cv"))
 
   private val f2jBLAS = new F2jBLAS
 
   /** @group setParam */
+  @Since("1.2.0")
   def setEstimator(value: Estimator[_]): this.type = set(estimator, value)
 
   /** @group setParam */
+  @Since("1.2.0")
   def setEstimatorParamMaps(value: Array[ParamMap]): this.type = set(estimatorParamMaps, value)
 
   /** @group setParam */
+  @Since("1.2.0")
   def setEvaluator(value: Evaluator): this.type = set(evaluator, value)
 
   /** @group setParam */
+  @Since("1.2.0")
   def setNumFolds(value: Int): this.type = set(numFolds, value)
-
+  @Since("1.2.0")
   override def fit(dataset: DataFrame): CrossValidatorModel = {
     val schema = dataset.schema
     transformSchema(schema, logging = true)
@@ -108,11 +114,11 @@ class CrossValidator(override val uid: String) extends Estimator[CrossValidatorM
     val bestModel = est.fit(dataset, epm(bestIndex)).asInstanceOf[Model[_]]
     copyValues(new CrossValidatorModel(uid, bestModel, metrics).setParent(this))
   }
-
+  @Since("1.2.0")
   override def transformSchema(schema: StructType): StructType = {
     $(estimator).transformSchema(schema)
   }
-
+  @Since("1.4.0")
   override def validateParams(): Unit = {
     super.validateParams()
     val est = $(estimator)
@@ -120,7 +126,7 @@ class CrossValidator(override val uid: String) extends Estimator[CrossValidatorM
       est.copy(paramMap).validateParams()
     }
   }
-
+  @Since("1.4.0")
   override def copy(extra: ParamMap): CrossValidator = {
     val copied = defaultCopy(extra).asInstanceOf[CrossValidator]
     if (copied.isDefined(estimator)) {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala
index 98a8f0330ca4..6b6fcb093a87 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala
@@ -20,13 +20,14 @@ package org.apache.spark.ml.tuning
 import scala.annotation.varargs
 import scala.collection.mutable
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.param._
 
 /**
  * :: Experimental ::
  * Builder for a param grid used in grid search-based model selection.
  */
+@Since("1.2.0")
 @Experimental
 class ParamGridBuilder {
 
@@ -35,6 +36,7 @@ class ParamGridBuilder {
   /**
    * Sets the given parameters in this grid to fixed values.
    */
+  @Since("1.2.0")
   def baseOn(paramMap: ParamMap): this.type = {
     baseOn(paramMap.toSeq: _*)
     this
@@ -43,6 +45,7 @@ class ParamGridBuilder {
   /**
    * Sets the given parameters in this grid to fixed values.
    */
+  @Since("1.2.0")
   @varargs
   def baseOn(paramPairs: ParamPair[_]*): this.type = {
     paramPairs.foreach { p =>
@@ -54,6 +57,7 @@ class ParamGridBuilder {
   /**
    * Adds a param with multiple values (overwrites if the input param exists).
    */
+  @Since("1.2.0")
   def addGrid[T](param: Param[T], values: Iterable[T]): this.type = {
     paramGrid.put(param, values)
     this
@@ -64,6 +68,7 @@ class ParamGridBuilder {
   /**
    * Adds a double param with multiple values.
    */
+  @Since("1.2.0")
   def addGrid(param: DoubleParam, values: Array[Double]): this.type = {
     addGrid[Double](param, values)
   }
@@ -71,6 +76,7 @@ class ParamGridBuilder {
   /**
    * Adds a int param with multiple values.
    */
+  @Since("1.2.0")
   def addGrid(param: IntParam, values: Array[Int]): this.type = {
     addGrid[Int](param, values)
   }
@@ -78,6 +84,7 @@ class ParamGridBuilder {
   /**
    * Adds a float param with multiple values.
    */
+  @Since("1.2.0")
   def addGrid(param: FloatParam, values: Array[Float]): this.type = {
     addGrid[Float](param, values)
   }
@@ -85,6 +92,7 @@ class ParamGridBuilder {
   /**
    * Adds a long param with multiple values.
    */
+  @Since("1.2.0")
   def addGrid(param: LongParam, values: Array[Long]): this.type = {
     addGrid[Long](param, values)
   }
@@ -92,6 +100,7 @@ class ParamGridBuilder {
   /**
    * Adds a boolean param with true and false.
    */
+  @Since("1.2.0")
   def addGrid(param: BooleanParam): this.type = {
     addGrid[Boolean](param, Array(true, false))
   }
@@ -99,6 +108,7 @@ class ParamGridBuilder {
   /**
    * Builds and returns all combinations of parameters specified by the param grid.
    */
+  @Since("1.2.0")
   def build(): Array[ParamMap] = {
     var paramMaps = Array(new ParamMap)
     paramGrid.foreach { case (param, values) =>
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala
index 73a14b831015..4a900c4fd432 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.ml.tuning
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.evaluation.Evaluator
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.param.{DoubleParam, ParamMap, ParamValidators}
@@ -51,24 +51,30 @@ private[ml] trait TrainValidationSplitParams extends ValidatorParams {
  * and uses evaluation metric on the validation set to select the best model.
  * Similar to [[CrossValidator]], but only splits the set once.
  */
+@Since("1.5.0")
 @Experimental
-class TrainValidationSplit(override val uid: String) extends Estimator[TrainValidationSplitModel]
+class TrainValidationSplit @Since("1.5.0") (@Since("1.5.0") override val uid: String)
+  extends Estimator[TrainValidationSplitModel]
   with TrainValidationSplitParams with Logging {
-
+  @Since("1.5.0")
   def this() = this(Identifiable.randomUID("tvs"))
 
   /** @group setParam */
+  @Since("1.5.0")
   def setEstimator(value: Estimator[_]): this.type = set(estimator, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setEstimatorParamMaps(value: Array[ParamMap]): this.type = set(estimatorParamMaps, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setEvaluator(value: Evaluator): this.type = set(evaluator, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setTrainRatio(value: Double): this.type = set(trainRatio, value)
-
+  @Since("1.5.0")
   override def fit(dataset: DataFrame): TrainValidationSplitModel = {
     val schema = dataset.schema
     transformSchema(schema, logging = true)
@@ -107,11 +113,11 @@ class TrainValidationSplit(override val uid: String) extends Estimator[TrainVali
     val bestModel = est.fit(dataset, epm(bestIndex)).asInstanceOf[Model[_]]
     copyValues(new TrainValidationSplitModel(uid, bestModel, metrics).setParent(this))
   }
-
+  @Since("1.5.0")
   override def transformSchema(schema: StructType): StructType = {
     $(estimator).transformSchema(schema)
   }
-
+  @Since("1.5.0")
   override def validateParams(): Unit = {
     super.validateParams()
     val est = $(estimator)
@@ -119,7 +125,7 @@ class TrainValidationSplit(override val uid: String) extends Estimator[TrainVali
       est.copy(paramMap).validateParams()
     }
   }
-
+  @Since("1.5.0")
   override def copy(extra: ParamMap): TrainValidationSplit = {
     val copied = defaultCopy(extra).asInstanceOf[TrainValidationSplit]
     if (copied.isDefined(estimator)) {

From eb4f58061d93ab9e4fff4500d1db5169d087d1ba Mon Sep 17 00:00:00 2001
From: "Ehsan M.Kermani" <ehsanmo1367@gmail.com>
Date: Tue, 20 Oct 2015 17:20:25 -0700
Subject: [PATCH 3/5] indentations fixed

---
 .../scala/org/apache/spark/ml/regression/GBTRegressor.scala | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index 662ada9f3cde..d99f327b392f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -161,9 +161,9 @@ object GBTRegressor {
 @Since("1.4.0")
 @Experimental
 final class GBTRegressionModel @Since("1.4.0") (
-                                                 @Since("1.4.0") override val uid: String,
-                                                 private val _trees: Array[DecisionTreeRegressionModel],
-                                                 private val _treeWeights: Array[Double])
+  @Since("1.4.0") override val uid: String,
+  private val _trees: Array[DecisionTreeRegressionModel],
+  private val _treeWeights: Array[Double])
   extends PredictionModel[Vector, GBTRegressionModel]
   with TreeEnsembleModel with Serializable {
 

From 338bbf8b00bacc588247b54de1e17800deb95fbb Mon Sep 17 00:00:00 2001
From: "Ehsan M.Kermani" <ehsanmo1367@gmail.com>
Date: Wed, 21 Oct 2015 12:53:25 -0700
Subject: [PATCH 4/5] coding style fixed

---
 .../org/apache/spark/ml/regression/DecisionTreeRegressor.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
index 77d47e5458e9..b61495286f07 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -36,7 +36,7 @@ import org.apache.spark.sql.DataFrame
  * for regression.
  * It supports both continuous and categorical features.
  */
-@Since("1.4.0") 
+@Since("1.4.0")
 @Experimental
 final class DecisionTreeRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
   extends Predictor[Vector, DecisionTreeRegressor, DecisionTreeRegressionModel]

From 27c9cc83b571cbab11905849c8c3034844a9a232 Mon Sep 17 00:00:00 2001
From: "Ehsan M.Kermani" <ehsanmo1367@gmail.com>
Date: Tue, 27 Oct 2015 10:23:33 -0700
Subject: [PATCH 5/5] renewed commits for JIRA-10266

---
 .../ml/regression/DecisionTreeRegressor.scala  |  2 +-
 .../spark/ml/regression/GBTRegressor.scala     | 18 +++---------------
 .../spark/ml/regression/LinearRegression.scala |  4 ----
 3 files changed, 4 insertions(+), 20 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
index 4d7ddba9bb89..705db5c445e1 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -56,7 +56,7 @@ final class DecisionTreeRegressor @Since("1.4.0") (@Since("1.4.0") override val
   override def setMinInfoGain(value: Double): this.type = super.setMinInfoGain(value)
   @Since("1.4.0")
   override def setMaxMemoryInMB(value: Int): this.type = super.setMaxMemoryInMB(value)
-  @Since("1.4.0")  
+  @Since("1.4.0")
   override def setCacheNodeIds(value: Boolean): this.type = super.setCacheNodeIds(value)
   @Since("1.4.0")
   override def setCheckpointInterval(value: Int): this.type = super.setCheckpointInterval(value)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index 1d90e6a4da39..9d648739b89a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -159,29 +159,18 @@ object GBTRegressor {
  * @param _trees  Decision trees in the ensemble.
  * @param _treeWeights  Weights for the decision trees in the ensemble.
  */
-@Since("1.4.0")
 @Experimental
-<<<<<<< HEAD
-final class GBTRegressionModel @Since("1.4.0") (
-  @Since("1.4.0") override val uid: String,
-  private val _trees: Array[DecisionTreeRegressionModel],
-  private val _treeWeights: Array[Double])
-=======
 final class GBTRegressionModel private[ml](
     override val uid: String,
     private val _trees: Array[DecisionTreeRegressionModel],
     private val _treeWeights: Array[Double],
     override val numFeatures: Int)
->>>>>>> 360ed832f5213b805ac28cf1d2828be09480f2d6
   extends PredictionModel[Vector, GBTRegressionModel]
   with TreeEnsembleModel with Serializable {
 
   require(numTrees > 0, "GBTRegressionModel requires at least 1 tree.")
   require(_trees.length == _treeWeights.length, "GBTRegressionModel given trees, treeWeights of" +
     s" non-matching lengths (${_trees.length}, ${_treeWeights.length}, respectively).")
-<<<<<<< HEAD
-  @Since("1.4.0")
-=======
 
   /**
    * Construct a GBTRegressionModel
@@ -191,9 +180,8 @@ final class GBTRegressionModel private[ml](
   def this(uid: String, _trees: Array[DecisionTreeRegressionModel], _treeWeights: Array[Double]) =
     this(uid, _trees, _treeWeights, -1)
 
->>>>>>> 360ed832f5213b805ac28cf1d2828be09480f2d6
   override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
-  @Since("1.4.0")
+
   override def treeWeights: Array[Double] = _treeWeights
 
   override protected def transformImpl(dataset: DataFrame): DataFrame = {
@@ -210,12 +198,12 @@ final class GBTRegressionModel private[ml](
     val treePredictions = _trees.map(_.rootNode.predictImpl(features).prediction)
     blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1)
   }
-  @Since("1.4.0")
+
   override def copy(extra: ParamMap): GBTRegressionModel = {
     copyValues(new GBTRegressionModel(uid, _trees, _treeWeights, numFeatures),
       extra).setParent(parent)
   }
-  @Since("1.4.0")
+
   override def toString: String = {
     s"GBTRegressionModel (uid=$uid) with $numTrees trees"
   }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 27292399b11c..19f940c2b7e8 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -23,13 +23,9 @@ import breeze.linalg.{DenseVector => BDV}
 import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN}
 
 import org.apache.spark.{Logging, SparkException}
-<<<<<<< HEAD
 import org.apache.spark.annotation.{Experimental, Since}
-=======
-import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.feature.Instance
 import org.apache.spark.ml.optim.WeightedLeastSquares
->>>>>>> 360ed832f5213b805ac28cf1d2828be09480f2d6
 import org.apache.spark.ml.PredictorParams
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.param.shared._