[SPARK-10259][ML] Add @since annotation to ml.classification

Add since annotation to ml.classification Author: Takahashi Hiroshi <takahashi.hiroshi@lab.ntt.co.jp> Closes #8534 from taishi-oss/issue10259.

[SPARK-10259][ML] Add @since annotation to ml.classification
7d05a624 · Takahashi Hiroshi · Xiangrui Meng · 73896588 · 7d05a624 · 7d05a624
Commit 7d05a624 authored 9 years ago by Takahashi Hiroshi Committed by Xiangrui Meng 9 years ago
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -17,7 +17,7 @@
 package org.apache.spark.ml.classification
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{DecisionTreeModel, DecisionTreeParams, Node, TreeClassifierParams}
 import org.apache.spark.ml.tree.impl.RandomForest
@@ -36,32 +36,44 @@ import org.apache.spark.sql.DataFrame
 * It supports both binary and multiclass labels, as well as both continuous and categorical
 * features.
 */
+@Since("1.4.0")
 @Experimental
-final class DecisionTreeClassifier(override val uid: String)
+final class DecisionTreeClassifier @Since("1.4.0") (
+    @Since("1.4.0") override val uid: String)
  extends ProbabilisticClassifier[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel]
  with DecisionTreeParams with TreeClassifierParams {
+  @Since("1.4.0")
  def this() = this(Identifiable.randomUID("dtc"))
  // 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)
+  @Since("1.6.0")
  override def setSeed(value: Long): this.type = super.setSeed(value)
  override protected def train(dataset: DataFrame): DecisionTreeClassificationModel = {
@@ -89,12 +101,15 @@ final class DecisionTreeClassifier(override val uid: String)
      subsamplingRate = 1.0)
  }
+  @Since("1.4.1")
  override def copy(extra: ParamMap): DecisionTreeClassifier = defaultCopy(extra)
 }
+@Since("1.4.0")
 @Experimental
 object DecisionTreeClassifier {
  /** Accessor for supported impurities: entropy, gini */
+  @Since("1.4.0")
  final val supportedImpurities: Array[String] = TreeClassifierParams.supportedImpurities
 }
@@ -104,12 +119,13 @@ object DecisionTreeClassifier {
 * It supports both binary and multiclass labels, as well as both continuous and categorical
 * features.
 */
+@Since("1.4.0")
 @Experimental
 final class DecisionTreeClassificationModel private[ml] (
-    override val uid: String,
+    @Since("1.4.0")override val uid: String,
-    override val rootNode: Node,
+    @Since("1.4.0")override val rootNode: Node,
-    override val numFeatures: Int,
+    @Since("1.6.0")override val numFeatures: Int,
-    override val numClasses: Int)
+    @Since("1.5.0")override val numClasses: Int)
  extends ProbabilisticClassificationModel[Vector, DecisionTreeClassificationModel]
  with DecisionTreeModel with Serializable {
@@ -142,11 +158,13 @@ final class DecisionTreeClassificationModel private[ml] (
    }
  }
+  @Since("1.4.0")
  override def copy(extra: ParamMap): DecisionTreeClassificationModel = {
    copyValues(new DecisionTreeClassificationModel(uid, rootNode, numFeatures, numClasses), extra)
      .setParent(parent)
  }
+  @Since("1.4.0")
  override def toString: String = {
    s"DecisionTreeClassificationModel (uid=$uid) of depth $depth with $numNodes nodes"
  }

--- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.classification
 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.regression.DecisionTreeRegressionModel
@@ -44,36 +44,47 @@ import org.apache.spark.sql.types.DoubleType
 * It supports binary labels, as well as both continuous and categorical features.
 * Note: Multiclass labels are not currently supported.
 */
+@Since("1.4.0")
 @Experimental
-final class GBTClassifier(override val uid: String)
+final class GBTClassifier @Since("1.4.0") (
+    @Since("1.4.0") override val uid: String)
  extends Predictor[Vector, GBTClassifier, GBTClassificationModel]
  with GBTParams with TreeClassifierParams with Logging {
+  @Since("1.4.0")
  def this() = this(Identifiable.randomUID("gbtc"))
  // Override parameter setters from parent trait for Java API compatibility.
  // Parameters from TreeClassifierParams:
+  @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("GBTClassifier.setImpurity should NOT be used")
    this
@@ -81,8 +92,10 @@ final class GBTClassifier(override val uid: String)
  // 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)
@@ -90,8 +103,10 @@ final class GBTClassifier(override val uid: String)
  // 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 GBTClassifier:
@@ -102,6 +117,7 @@ final class GBTClassifier(override val uid: String)
   * (default = logistic)
   * @group param
   */
+  @Since("1.4.0")
  val lossType: Param[String] = new Param[String](this, "lossType", "Loss function which GBT" +
    " tries to minimize (case-insensitive). Supported options:" +
    s" ${GBTClassifier.supportedLossTypes.mkString(", ")}",
@@ -110,9 +126,11 @@ final class GBTClassifier(override val uid: String)
  setDefault(lossType -> "logistic")
  /** @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. */
@@ -145,13 +163,16 @@ final class GBTClassifier(override val uid: String)
    GBTClassificationModel.fromOld(oldModel, this, categoricalFeatures, numFeatures)
  }
+  @Since("1.4.1")
  override def copy(extra: ParamMap): GBTClassifier = defaultCopy(extra)
 }
+@Since("1.4.0")
 @Experimental
 object GBTClassifier {
  // The losses below should be lowercase.
  /** Accessor for supported loss settings: logistic */
+  @Since("1.4.0")
  final val supportedLossTypes: Array[String] = Array("logistic").map(_.toLowerCase)
 }
@@ -164,12 +185,13 @@ object GBTClassifier {
 * @param _trees  Decision trees in the ensemble.
 * @param _treeWeights  Weights for the decision trees in the ensemble.
 */
+@Since("1.6.0")
 @Experimental
 final class GBTClassificationModel private[ml](
-    override val uid: String,
+    @Since("1.6.0") override val uid: String,
    private val _trees: Array[DecisionTreeRegressionModel],
    private val _treeWeights: Array[Double],
-    override val numFeatures: Int)
+    @Since("1.6.0") override val numFeatures: Int)
  extends PredictionModel[Vector, GBTClassificationModel]
  with TreeEnsembleModel with Serializable {
@@ -182,11 +204,14 @@ final class GBTClassificationModel private[ml](
   * @param _trees  Decision trees in the ensemble.
   * @param _treeWeights  Weights for the decision trees in the ensemble.
   */
+  @Since("1.6.0")
  def this(uid: String, _trees: Array[DecisionTreeRegressionModel], _treeWeights: Array[Double]) =
    this(uid, _trees, _treeWeights, -1)
+  @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 = {
@@ -205,11 +230,13 @@ final class GBTClassificationModel private[ml](
    if (prediction > 0.0) 1.0 else 0.0
  }
+  @Since("1.4.0")
  override def copy(extra: ParamMap): GBTClassificationModel = {
    copyValues(new GBTClassificationModel(uid, _trees, _treeWeights, numFeatures),
      extra).setParent(parent)
  }
+  @Since("1.4.0")
  override def toString: String = {
    s"GBTClassificationModel (uid=$uid) with $numTrees trees"
  }

--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -24,7 +24,7 @@ import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS,
 import org.apache.hadoop.fs.Path
 import org.apache.spark.{Logging, SparkException}
-import org.apache.spark.annotation.{Since, Experimental}
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.feature.Instance
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
@@ -154,11 +154,14 @@ private[classification] trait LogisticRegressionParams extends ProbabilisticClas
 * Currently, this class only supports binary classification.  It will support multiclass
 * in the future.
 */
+@Since("1.2.0")
 @Experimental
-class LogisticRegression(override val uid: String)
+class LogisticRegression @Since("1.2.0") (
+    @Since("1.4.0") override val uid: String)
  extends ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel]
  with LogisticRegressionParams with DefaultParamsWritable with Logging {
+  @Since("1.4.0")
  def this() = this(Identifiable.randomUID("logreg"))
  /**
@@ -166,6 +169,7 @@ class LogisticRegression(override val uid: String)
   * Default is 0.0.
   * @group setParam
   */
+  @Since("1.2.0")
  def setRegParam(value: Double): this.type = set(regParam, value)
  setDefault(regParam -> 0.0)
@@ -176,6 +180,7 @@ class LogisticRegression(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)
@@ -184,6 +189,7 @@ class LogisticRegression(override val uid: String)
   * Default is 100.
   * @group setParam
   */
+  @Since("1.2.0")
  def setMaxIter(value: Int): this.type = set(maxIter, value)
  setDefault(maxIter -> 100)
@@ -193,6 +199,7 @@ class LogisticRegression(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)
@@ -201,6 +208,7 @@ class LogisticRegression(override val uid: String)
   * Default is true.
   * @group setParam
   */
+  @Since("1.4.0")
  def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
  setDefault(fitIntercept -> true)
@@ -213,11 +221,14 @@ class LogisticRegression(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)
+  @Since("1.5.0")
  override def setThreshold(value: Double): this.type = super.setThreshold(value)
+  @Since("1.5.0")
  override def getThreshold: Double = super.getThreshold
  /**
@@ -226,11 +237,14 @@ class LogisticRegression(override val uid: String)
   * Default is empty, so all instances have weight one.
   * @group setParam
   */
+  @Since("1.6.0")
  def setWeightCol(value: String): this.type = set(weightCol, value)
  setDefault(weightCol -> "")
+  @Since("1.5.0")
  override def setThresholds(value: Array[Double]): this.type = super.setThresholds(value)
+  @Since("1.5.0")
  override def getThresholds: Array[Double] = super.getThresholds
  override protected def train(dataset: DataFrame): LogisticRegressionModel = {
@@ -384,11 +398,14 @@ class LogisticRegression(override val uid: String)
    model.setSummary(logRegSummary)
  }
+  @Since("1.4.0")
  override def copy(extra: ParamMap): LogisticRegression = defaultCopy(extra)
 }
+@Since("1.6.0")
 object LogisticRegression extends DefaultParamsReadable[LogisticRegression] {
+  @Since("1.6.0")
  override def load(path: String): LogisticRegression = super.load(path)
 }
@@ -396,23 +413,28 @@ object LogisticRegression extends DefaultParamsReadable[LogisticRegression] {
 * :: Experimental ::
 * Model produced by [[LogisticRegression]].
 */
+@Since("1.4.0")
 @Experimental
 class LogisticRegressionModel private[ml] (
-    override val uid: String,
+    @Since("1.4.0") override val uid: String,
-    val coefficients: Vector,
+    @Since("1.6.0") val coefficients: Vector,
-    val intercept: Double)
+    @Since("1.3.0") val intercept: Double)
  extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel]
  with LogisticRegressionParams with MLWritable {
  @deprecated("Use coefficients instead.", "1.6.0")
  def weights: Vector = coefficients
+  @Since("1.5.0")
  override def setThreshold(value: Double): this.type = super.setThreshold(value)
+  @Since("1.5.0")
  override def getThreshold: Double = super.getThreshold
+  @Since("1.5.0")
  override def setThresholds(value: Array[Double]): this.type = super.setThresholds(value)
+  @Since("1.5.0")
  override def getThresholds: Array[Double] = super.getThresholds
  /** Margin (rawPrediction) for class label 1.  For binary classification only. */
@@ -426,8 +448,10 @@ class LogisticRegressionModel private[ml] (
    1.0 / (1.0 + math.exp(-m))
  }
+  @Since("1.6.0")
  override val numFeatures: Int = coefficients.size
+  @Since("1.3.0")
  override val numClasses: Int = 2
  private var trainingSummary: Option[LogisticRegressionTrainingSummary] = None
@@ -436,6 +460,7 @@ class LogisticRegressionModel private[ml] (
   * Gets summary of model on training set. An exception is
   * thrown if `trainingSummary == None`.
   */
+  @Since("1.5.0")
  def summary: LogisticRegressionTrainingSummary = trainingSummary match {
    case Some(summ) => summ
    case None =>
@@ -451,6 +476,7 @@ class LogisticRegressionModel private[ml] (
  }
  /** Indicates whether a training summary exists for this model instance. */
+  @Since("1.5.0")
  def hasSummary: Boolean = trainingSummary.isDefined
  /**
@@ -493,6 +519,7 @@ class LogisticRegressionModel private[ml] (
    Vectors.dense(-m, m)
  }
+  @Since("1.4.0")
  override def copy(extra: ParamMap): LogisticRegressionModel = {
    val newModel = copyValues(new LogisticRegressionModel(uid, coefficients, intercept), extra)
    if (trainingSummary.isDefined) newModel.setSummary(trainingSummary.get)
@@ -710,12 +737,13 @@ sealed trait LogisticRegressionSummary extends Serializable {
 * @param objectiveHistory objective function (scaled loss + regularization) at each iteration.
 */
 @Experimental
+@Since("1.5.0")
 class BinaryLogisticRegressionTrainingSummary private[classification] (
-    predictions: DataFrame,
+    @Since("1.5.0") predictions: DataFrame,
-    probabilityCol: String,
+    @Since("1.5.0") probabilityCol: String,
-    labelCol: String,
+    @Since("1.5.0") labelCol: String,
-    featuresCol: String,
+    @Since("1.6.0") featuresCol: String,
-    val objectiveHistory: Array[Double])
+    @Since("1.5.0") val objectiveHistory: Array[Double])
  extends BinaryLogisticRegressionSummary(predictions, probabilityCol, labelCol, featuresCol)
  with LogisticRegressionTrainingSummary {
@@ -731,11 +759,13 @@ class BinaryLogisticRegressionTrainingSummary private[classification] (
 * @param featuresCol field in "predictions" which gives the features of each instance as a vector.
 */
 @Experimental
+@Since("1.5.0")
 class BinaryLogisticRegressionSummary private[classification] (
-    @transient override val predictions: DataFrame,
+    @Since("1.5.0") @transient override val predictions: DataFrame,
-    override val probabilityCol: String,
+    @Since("1.5.0") override val probabilityCol: String,
-    override val labelCol: String,
+    @Since("1.5.0") override val labelCol: String,
-    override val featuresCol: String) extends LogisticRegressionSummary {
+    @Since("1.6.0") override val featuresCol: String) extends LogisticRegressionSummary {
  private val sqlContext = predictions.sqlContext
  import sqlContext.implicits._
@@ -760,6 +790,7 @@ class BinaryLogisticRegressionSummary private[classification] (
   *       This will change in later Spark versions.
   * @see http://en.wikipedia.org/wiki/Receiver_operating_characteristic
   */
+  @Since("1.5.0")
  @transient lazy val roc: DataFrame = binaryMetrics.roc().toDF("FPR", "TPR")
  /**
@@ -768,6 +799,7 @@ class BinaryLogisticRegressionSummary private[classification] (
   * Note: This ignores instance weights (setting all to 1.0) from [[LogisticRegression.weightCol]].
   *       This will change in later Spark versions.
   */
+  @Since("1.5.0")
  lazy val areaUnderROC: Double = binaryMetrics.areaUnderROC()
  /**
@@ -777,6 +809,7 @@ class BinaryLogisticRegressionSummary private[classification] (
   * Note: This ignores instance weights (setting all to 1.0) from [[LogisticRegression.weightCol]].
   *       This will change in later Spark versions.
   */
+  @Since("1.5.0")
  @transient lazy val pr: DataFrame = binaryMetrics.pr().toDF("recall", "precision")
  /**
@@ -785,6 +818,7 @@ class BinaryLogisticRegressionSummary private[classification] (
   * Note: This ignores instance weights (setting all to 1.0) from [[LogisticRegression.weightCol]].
   *       This will change in later Spark versions.
   */
+  @Since("1.5.0")
  @transient lazy val fMeasureByThreshold: DataFrame = {
    binaryMetrics.fMeasureByThreshold().toDF("threshold", "F-Measure")
  }
@@ -797,6 +831,7 @@ class BinaryLogisticRegressionSummary private[classification] (
   * Note: This ignores instance weights (setting all to 1.0) from [[LogisticRegression.weightCol]].
   *       This will change in later Spark versions.
   */
+  @Since("1.5.0")
  @transient lazy val precisionByThreshold: DataFrame = {
    binaryMetrics.precisionByThreshold().toDF("threshold", "precision")
  }
@@ -809,6 +844,7 @@ class BinaryLogisticRegressionSummary private[classification] (
   * Note: This ignores instance weights (setting all to 1.0) from [[LogisticRegression.weightCol]].
   *       This will change in later Spark versions.
   */
+  @Since("1.5.0")
  @transient lazy val recallByThreshold: DataFrame = {
    binaryMetrics.recallByThreshold().toDF("threshold", "recall")
  }

--- a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
@@ -19,7 +19,7 @@ package org.apache.spark.ml.classification
 import scala.collection.JavaConverters._
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.param.shared.{HasTol, HasMaxIter, HasSeed}
 import org.apache.spark.ml.{PredictorParams, PredictionModel, Predictor}
 import org.apache.spark.ml.param.{IntParam, ParamValidators, IntArrayParam, ParamMap}
@@ -104,19 +104,23 @@ private object LabelConverter {
 * Each layer has sigmoid activation function, output layer has softmax.
 * Number of inputs has to be equal to the size of feature vectors.
 * Number of outputs has to be equal to the total number of labels.
- *
 */
+@Since("1.5.0")
 @Experimental
-class MultilayerPerceptronClassifier(override val uid: String)
+class MultilayerPerceptronClassifier @Since("1.5.0") (
+    @Since("1.5.0") override val uid: String)
  extends Predictor[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel]
  with MultilayerPerceptronParams {
+  @Since("1.5.0")
  def this() = this(Identifiable.randomUID("mlpc"))
  /** @group setParam */
+  @Since("1.5.0")
  def setLayers(value: Array[Int]): this.type = set(layers, value)
  /** @group setParam */
+  @Since("1.5.0")
  def setBlockSize(value: Int): this.type = set(blockSize, value)
  /**
@@ -124,6 +128,7 @@ class MultilayerPerceptronClassifier(override val uid: String)
   * Default is 100.
   * @group setParam
   */
+  @Since("1.5.0")
  def setMaxIter(value: Int): this.type = set(maxIter, value)
  /**
@@ -132,14 +137,17 @@ class MultilayerPerceptronClassifier(override val uid: String)
   * Default is 1E-4.
   * @group setParam
   */
+  @Since("1.5.0")
  def setTol(value: Double): this.type = set(tol, value)
  /**
   * Set the seed for weights initialization.
   * @group setParam
   */
+  @Since("1.5.0")
  def setSeed(value: Long): this.type = set(seed, value)
+  @Since("1.5.0")
  override def copy(extra: ParamMap): MultilayerPerceptronClassifier = defaultCopy(extra)
  /**
@@ -173,14 +181,16 @@ class MultilayerPerceptronClassifier(override val uid: String)
 * @param weights vector of initial weights for the model that consists of the weights of layers
 * @return prediction model
 */
+@Since("1.5.0")
 @Experimental
 class MultilayerPerceptronClassificationModel private[ml] (
-    override val uid: String,
+    @Since("1.5.0") override val uid: String,
-    val layers: Array[Int],
+    @Since("1.5.0") val layers: Array[Int],
-    val weights: Vector)
+    @Since("1.5.0") val weights: Vector)
  extends PredictionModel[Vector, MultilayerPerceptronClassificationModel]
  with Serializable {
+  @Since("1.6.0")
  override val numFeatures: Int = layers.head
  private val mlpModel = FeedForwardTopology.multiLayerPerceptron(layers, true).getInstance(weights)
@@ -200,6 +210,7 @@ class MultilayerPerceptronClassificationModel private[ml] (
    LabelConverter.decodeLabel(mlpModel.predict(features))
  }
+  @Since("1.5.0")
  override def copy(extra: ParamMap): MultilayerPerceptronClassificationModel = {
    copyValues(new MultilayerPerceptronClassificationModel(uid, layers, weights), extra)
  }

--- a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
@@ -72,11 +72,14 @@ private[ml] trait NaiveBayesParams extends PredictorParams {
 * ([[http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html]]).
 * The input feature values must be nonnegative.
 */
+@Since("1.5.0")
 @Experimental
-class NaiveBayes(override val uid: String)
+class NaiveBayes @Since("1.5.0") (
+    @Since("1.5.0") override val uid: String)
  extends ProbabilisticClassifier[Vector, NaiveBayes, NaiveBayesModel]
  with NaiveBayesParams with DefaultParamsWritable {
+  @Since("1.5.0")
  def this() = this(Identifiable.randomUID("nb"))
  /**
@@ -84,6 +87,7 @@ class NaiveBayes(override val uid: String)
   * Default is 1.0.
   * @group setParam
   */
+  @Since("1.5.0")
  def setSmoothing(value: Double): this.type = set(smoothing, value)
  setDefault(smoothing -> 1.0)
@@ -93,6 +97,7 @@ class NaiveBayes(override val uid: String)
   * Default is "multinomial"
   * @group setParam
   */
+  @Since("1.5.0")
  def setModelType(value: String): this.type = set(modelType, value)
  setDefault(modelType -> OldNaiveBayes.Multinomial)
@@ -102,6 +107,7 @@ class NaiveBayes(override val uid: String)
    NaiveBayesModel.fromOld(oldModel, this)
  }
+  @Since("1.5.0")
  override def copy(extra: ParamMap): NaiveBayes = defaultCopy(extra)
 }
@@ -119,11 +125,12 @@ object NaiveBayes extends DefaultParamsReadable[NaiveBayes] {
 * @param theta log of class conditional probabilities, whose dimension is C (number of classes)
 *              by D (number of features)
 */
+@Since("1.5.0")
 @Experimental
 class NaiveBayesModel private[ml] (
-    override val uid: String,
+    @Since("1.5.0") override val uid: String,
-    val pi: Vector,
+    @Since("1.5.0") val pi: Vector,
-    val theta: Matrix)
+    @Since("1.5.0") val theta: Matrix)
  extends ProbabilisticClassificationModel[Vector, NaiveBayesModel]
  with NaiveBayesParams with MLWritable {
@@ -148,8 +155,10 @@ class NaiveBayesModel private[ml] (
      throw new UnknownError(s"Invalid modelType: ${$(modelType)}.")
  }
+  @Since("1.6.0")
  override val numFeatures: Int = theta.numCols
+  @Since("1.5.0")
  override val numClasses: Int = pi.size
  private def multinomialCalculation(features: Vector) = {
@@ -206,10 +215,12 @@ class NaiveBayesModel private[ml] (
    }
  }
+  @Since("1.5.0")
  override def copy(extra: ParamMap): NaiveBayesModel = {
    copyValues(new NaiveBayesModel(uid, pi, theta).setParent(this.parent), extra)
  }
+  @Since("1.5.0")
  override def toString: String = {
    s"NaiveBayesModel (uid=$uid) with ${pi.size} classes"
  }

--- a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
@@ -21,7 +21,7 @@ import java.util.UUID
 import scala.language.existentials
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml._
 import org.apache.spark.ml.attribute._
 import org.apache.spark.ml.param.{Param, ParamMap}
@@ -70,17 +70,20 @@ private[ml] trait OneVsRestParams extends PredictorParams {
 *               The i-th model is produced by testing the i-th class (taking label 1) vs the rest
 *               (taking label 0).
 */
+@Since("1.4.0")
 @Experimental
 final class OneVsRestModel private[ml] (
-    override val uid: String,
+    @Since("1.4.0")  override val uid: String,
-    labelMetadata: Metadata,
+    @Since("1.4.0") labelMetadata: Metadata,
-    val models: Array[_ <: ClassificationModel[_, _]])
+    @Since("1.4.0") val models: Array[_ <: ClassificationModel[_, _]])
  extends Model[OneVsRestModel] with OneVsRestParams {
+  @Since("1.4.0")
  override def transformSchema(schema: StructType): StructType = {
    validateAndTransformSchema(schema, fitting = false, getClassifier.featuresDataType)
  }
+  @Since("1.4.0")
  override def transform(dataset: DataFrame): DataFrame = {
    // Check schema
    transformSchema(dataset.schema, logging = true)
@@ -134,6 +137,7 @@ final class OneVsRestModel private[ml] (
      .drop(accColName)
  }
+  @Since("1.4.1")
  override def copy(extra: ParamMap): OneVsRestModel = {
    val copied = new OneVsRestModel(
      uid, labelMetadata, models.map(_.copy(extra).asInstanceOf[ClassificationModel[_, _]]))
@@ -150,30 +154,39 @@ final class OneVsRestModel private[ml] (
 * Each example is scored against all k models and the model with highest score
 * is picked to label the example.
 */
+@Since("1.4.0")
 @Experimental
-final class OneVsRest(override val uid: String)
+final class OneVsRest @Since("1.4.0") (
+    @Since("1.4.0") override val uid: String)
  extends Estimator[OneVsRestModel] with OneVsRestParams {
+  @Since("1.4.0")
  def this() = this(Identifiable.randomUID("oneVsRest"))
  /** @group setParam */
+  @Since("1.4.0")
  def setClassifier(value: Classifier[_, _, _]): this.type = {
    set(classifier, value.asInstanceOf[ClassifierType])
  }
  /** @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)
+  @Since("1.4.0")
  override def transformSchema(schema: StructType): StructType = {
    validateAndTransformSchema(schema, fitting = true, getClassifier.featuresDataType)
  }
+  @Since("1.4.0")
  override def fit(dataset: DataFrame): OneVsRestModel = {
    // determine number of classes either from metadata if provided, or via computation.
    val labelSchema = dataset.schema($(labelCol))
@@ -222,6 +235,7 @@ final class OneVsRest(override val uid: String)
    copyValues(model)
  }
+  @Since("1.4.1")
  override def copy(extra: ParamMap): OneVsRest = {
    val copied = defaultCopy(extra).asInstanceOf[OneVsRest]
    if (isDefined(classifier)) {

--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -17,7 +17,7 @@
 package org.apache.spark.ml.classification
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.tree.impl.RandomForest
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{DecisionTreeModel, RandomForestParams, TreeClassifierParams, TreeEnsembleModel}
@@ -38,44 +38,59 @@ import org.apache.spark.sql.functions._
 * It supports both binary and multiclass labels, as well as both continuous and categorical
 * features.
 */
+@Since("1.4.0")
 @Experimental
-final class RandomForestClassifier(override val uid: String)
+final class RandomForestClassifier @Since("1.4.0") (
+    @Since("1.4.0") override val uid: String)
  extends ProbabilisticClassifier[Vector, RandomForestClassifier, RandomForestClassificationModel]
  with RandomForestParams with TreeClassifierParams {
+  @Since("1.4.0")
  def this() = this(Identifiable.randomUID("rfc"))
  // Override parameter setters from parent trait for Java API compatibility.
  // Parameters from TreeClassifierParams:
+  @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)
@@ -99,15 +114,19 @@ final class RandomForestClassifier(override val uid: String)
    new RandomForestClassificationModel(trees, numFeatures, numClasses)
  }
+  @Since("1.4.1")
  override def copy(extra: ParamMap): RandomForestClassifier = defaultCopy(extra)
 }
+@Since("1.4.0")
 @Experimental
 object RandomForestClassifier {
  /** Accessor for supported impurity settings: entropy, gini */
+  @Since("1.4.0")
  final val supportedImpurities: Array[String] = TreeClassifierParams.supportedImpurities
  /** Accessor for supported featureSubsetStrategy settings: auto, all, onethird, sqrt, log2 */
+  @Since("1.4.0")
  final val supportedFeatureSubsetStrategies: Array[String] =
    RandomForestParams.supportedFeatureSubsetStrategies
 }
@@ -120,12 +139,13 @@ object RandomForestClassifier {
 * @param _trees  Decision trees in the ensemble.
 *               Warning: These have null parents.
 */
+@Since("1.4.0")
 @Experimental
 final class RandomForestClassificationModel private[ml] (
-    override val uid: String,
+    @Since("1.5.0") override val uid: String,
    private val _trees: Array[DecisionTreeClassificationModel],
-    override val numFeatures: Int,
+    @Since("1.6.0") override val numFeatures: Int,
-    override val numClasses: Int)
+    @Since("1.5.0") override val numClasses: Int)
  extends ProbabilisticClassificationModel[Vector, RandomForestClassificationModel]
  with TreeEnsembleModel with Serializable {
@@ -141,11 +161,13 @@ final class RandomForestClassificationModel private[ml] (
      numClasses: Int) =
    this(Identifiable.randomUID("rfc"), trees, numFeatures, numClasses)
+  @Since("1.4.0")
  override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
  // Note: We may add support for weights (based on tree performance) later on.
  private lazy val _treeWeights: Array[Double] = Array.fill[Double](numTrees)(1.0)
+  @Since("1.4.0")
  override def treeWeights: Array[Double] = _treeWeights
  override protected def transformImpl(dataset: DataFrame): DataFrame = {
@@ -186,11 +208,13 @@ final class RandomForestClassificationModel private[ml] (
    }
  }
+  @Since("1.4.0")
  override def copy(extra: ParamMap): RandomForestClassificationModel = {
    copyValues(new RandomForestClassificationModel(uid, _trees, numFeatures, numClasses), extra)
      .setParent(parent)
  }
+  @Since("1.4.0")
  override def toString: String = {
    s"RandomForestClassificationModel (uid=$uid) with $numTrees trees"
  }