[SPARK-10266][Documentation, ML] Fixed @Since annotation for ml.tunning

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 */

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"
   }

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)
   }

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._
@@ -61,18 +61,20 @@ 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"))
 
   /**
    * Set the regularization parameter.
    * 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)
 }