[SPARK-8915] [DOCUMENTATION, MLLIB] Added @since tags to mllib.classification

Created since tags for methods in mllib.classification

Author: petz2000 <petz2000@gmail.com>

Closes #7371 from petz2000/add_since_mllib.classification and squashes the following commits:

39fe291 [petz2000] Removed whitespace in block comment
c9b1e03 [petz2000] Removed @since tags again from protected and private methods
cd759b6 [petz2000] Added @since tags to methods

4 files changed, 39 insertions, 0 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
index 35a0db76f3..ba73024e3c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
@@ -36,6 +36,7 @@ trait ClassificationModel extends Serializable {
    *
    * @param testData RDD representing data points to be predicted
    * @return an RDD[Double] where each entry contains the corresponding prediction
+   * @since 0.8.0
    */
   def predict(testData: RDD[Vector]): RDD[Double]
 
@@ -44,6 +45,7 @@ trait ClassificationModel extends Serializable {
    *
    * @param testData array representing a single data point
    * @return predicted category from the trained model
+   * @since 0.8.0
    */
   def predict(testData: Vector): Double
 
@@ -51,6 +53,7 @@ trait ClassificationModel extends Serializable {
    * Predict values for examples stored in a JavaRDD.
    * @param testData JavaRDD representing data points to be predicted
    * @return a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
+   * @since 0.8.0
    */
   def predict(testData: JavaRDD[Vector]): JavaRDD[java.lang.Double] =
     predict(testData.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]]
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
index 2df4d21e8c..268642ac6a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
@@ -85,6 +85,7 @@ class LogisticRegressionModel (
    * in Binary Logistic Regression. An example with prediction score greater than or equal to
    * this threshold is identified as an positive, and negative otherwise. The default value is 0.5.
    * It is only used for binary classification.
+   * @since 1.0.0
    */
   @Experimental
   def setThreshold(threshold: Double): this.type = {
@@ -96,6 +97,7 @@ class LogisticRegressionModel (
    * :: Experimental ::
    * Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
    * It is only used for binary classification.
+   * @since 1.3.0
    */
   @Experimental
   def getThreshold: Option[Double] = threshold
@@ -104,6 +106,7 @@ class LogisticRegressionModel (
    * :: Experimental ::
    * Clears the threshold so that `predict` will output raw prediction scores.
    * It is only used for binary classification.
+   * @since 1.0.0
    */
   @Experimental
   def clearThreshold(): this.type = {
@@ -155,6 +158,9 @@ class LogisticRegressionModel (
     }
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     GLMClassificationModel.SaveLoadV1_0.save(sc, path, this.getClass.getName,
       numFeatures, numClasses, weights, intercept, threshold)
@@ -162,6 +168,9 @@ class LogisticRegressionModel (
 
   override protected def formatVersion: String = "1.0"
 
+  /**
+   * @since 1.4.0
+   */
   override def toString: String = {
     s"${super.toString}, numClasses = ${numClasses}, threshold = ${threshold.getOrElse("None")}"
   }
@@ -169,6 +178,9 @@ class LogisticRegressionModel (
 
 object LogisticRegressionModel extends Loader[LogisticRegressionModel] {
 
+  /**
+   * @since 1.3.0
+   */
   override def load(sc: SparkContext, path: String): LogisticRegressionModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -249,6 +261,7 @@ object LogisticRegressionWithSGD {
    * @param miniBatchFraction Fraction of data to be used per iteration.
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
+   * @since 1.0.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -271,6 +284,7 @@ object LogisticRegressionWithSGD {
    * @param stepSize Step size to be used for each iteration of gradient descent.
 
    * @param miniBatchFraction Fraction of data to be used per iteration.
+   * @since 1.0.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -292,6 +306,7 @@ object LogisticRegressionWithSGD {
 
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LogisticRegressionModel which has the weights and offset from training.
+   * @since 1.0.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -309,6 +324,7 @@ object LogisticRegressionWithSGD {
    * @param input RDD of (label, array of features) pairs.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LogisticRegressionModel which has the weights and offset from training.
+   * @since 1.0.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -345,6 +361,7 @@ class LogisticRegressionWithLBFGS
    * Set the number of possible outcomes for k classes classification problem in
    * Multinomial Logistic Regression.
    * By default, it is binary logistic regression so k will be set to 2.
+   * @since 1.3.0
    */
   @Experimental
   def setNumClasses(numClasses: Int): this.type = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
index 8cf4e15efe..2df91c0942 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
@@ -444,6 +444,7 @@ object NaiveBayes {
    *
    * @param input RDD of `(label, array of features)` pairs.  Every vector should be a frequency
    *              vector or a count vector.
+   * @since 0.9.0
    */
   def train(input: RDD[LabeledPoint]): NaiveBayesModel = {
     new NaiveBayes().run(input)
@@ -459,6 +460,7 @@ object NaiveBayes {
    * @param input RDD of `(label, array of features)` pairs.  Every vector should be a frequency
    *              vector or a count vector.
    * @param lambda The smoothing parameter
+   * @since 0.9.0
    */
   def train(input: RDD[LabeledPoint], lambda: Double): NaiveBayesModel = {
     new NaiveBayes(lambda, Multinomial).run(input)
@@ -481,6 +483,7 @@ object NaiveBayes {
    *
    * @param modelType The type of NB model to fit from the enumeration NaiveBayesModels, can be
    *              multinomial or bernoulli
+   * @since 0.9.0
    */
   def train(input: RDD[LabeledPoint], lambda: Double, modelType: String): NaiveBayesModel = {
     require(supportedModelTypes.contains(modelType),
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
index 3484855607..5b54feeb10 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
@@ -46,6 +46,7 @@ class SVMModel (
    * Sets the threshold that separates positive predictions from negative predictions. An example
    * with prediction score greater than or equal to this threshold is identified as an positive,
    * and negative otherwise. The default value is 0.0.
+   * @since 1.3.0
    */
   @Experimental
   def setThreshold(threshold: Double): this.type = {
@@ -56,6 +57,7 @@ class SVMModel (
   /**
    * :: Experimental ::
    * Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
+   * @since 1.3.0
    */
   @Experimental
   def getThreshold: Option[Double] = threshold
@@ -63,6 +65,7 @@ class SVMModel (
   /**
    * :: Experimental ::
    * Clears the threshold so that `predict` will output raw prediction scores.
+   * @since 1.0.0
    */
   @Experimental
   def clearThreshold(): this.type = {
@@ -81,6 +84,9 @@ class SVMModel (
     }
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     GLMClassificationModel.SaveLoadV1_0.save(sc, path, this.getClass.getName,
       numFeatures = weights.size, numClasses = 2, weights, intercept, threshold)
@@ -88,6 +94,9 @@ class SVMModel (
 
   override protected def formatVersion: String = "1.0"
 
+  /**
+   * @since 1.4.0
+   */
   override def toString: String = {
     s"${super.toString}, numClasses = 2, threshold = ${threshold.getOrElse("None")}"
   }
@@ -95,6 +104,9 @@ class SVMModel (
 
 object SVMModel extends Loader[SVMModel] {
 
+   /**
+   * @since 1.3.0
+   */
   override def load(sc: SparkContext, path: String): SVMModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -173,6 +185,7 @@ object SVMWithSGD {
    * @param miniBatchFraction Fraction of data to be used per iteration.
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -196,6 +209,7 @@ object SVMWithSGD {
    * @param stepSize Step size to be used for each iteration of gradient descent.
    * @param regParam Regularization parameter.
    * @param miniBatchFraction Fraction of data to be used per iteration.
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -217,6 +231,7 @@ object SVMWithSGD {
    * @param regParam Regularization parameter.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a SVMModel which has the weights and offset from training.
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -235,6 +250,7 @@ object SVMWithSGD {
    * @param input RDD of (label, array of features) pairs.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a SVMModel which has the weights and offset from training.
+   * @since 0.8.0
    */
   def train(input: RDD[LabeledPoint], numIterations: Int): SVMModel = {
     train(input, numIterations, 1.0, 0.01, 1.0)