[SPARK-10241] [MLLIB] update since versions in mllib.recommendation

Same as #8421 but for `mllib.recommendation`.

cc srowen coderxiang

Author: Xiangrui Meng <meng@databricks.com>

Closes #8432 from mengxr/SPARK-10241.

(cherry picked from commit 086d4681df3ebfccfc04188262c10482f44553b0)
Signed-off-by: Xiangrui Meng <meng@databricks.com>

2 files changed, 25 insertions, 5 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index b27ef1b949..33aaf853e5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -28,7 +28,10 @@ import org.apache.spark.storage.StorageLevel
  * A more compact class to represent a rating than Tuple3[Int, Int, Double].
  */
 @Since("0.8.0")
-case class Rating(user: Int, product: Int, rating: Double)
+case class Rating @Since("0.8.0") (
+    @Since("0.8.0") user: Int,
+    @Since("0.8.0") product: Int,
+    @Since("0.8.0") rating: Double)
 
 /**
  * Alternating Least Squares matrix factorization.
@@ -59,6 +62,7 @@ case class Rating(user: Int, product: Int, rating: Double)
  * indicated user
  * preferences rather than explicit ratings given to items.
  */
+@Since("0.8.0")
 class ALS private (
     private var numUserBlocks: Int,
     private var numProductBlocks: Int,
@@ -74,6 +78,7 @@ class ALS private (
    * Constructs an ALS instance with default parameters: {numBlocks: -1, rank: 10, iterations: 10,
    * lambda: 0.01, implicitPrefs: false, alpha: 1.0}.
    */
+  @Since("0.8.0")
   def this() = this(-1, -1, 10, 10, 0.01, false, 1.0)
 
   /** If true, do alternating nonnegative least squares. */
@@ -90,6 +95,7 @@ class ALS private (
    * Set the number of blocks for both user blocks and product blocks to parallelize the computation
    * into; pass -1 for an auto-configured number of blocks. Default: -1.
    */
+  @Since("0.8.0")
   def setBlocks(numBlocks: Int): this.type = {
     this.numUserBlocks = numBlocks
     this.numProductBlocks = numBlocks
@@ -99,6 +105,7 @@ class ALS private (
   /**
    * Set the number of user blocks to parallelize the computation.
    */
+  @Since("1.1.0")
   def setUserBlocks(numUserBlocks: Int): this.type = {
     this.numUserBlocks = numUserBlocks
     this
@@ -107,30 +114,35 @@ class ALS private (
   /**
    * Set the number of product blocks to parallelize the computation.
    */
+  @Since("1.1.0")
   def setProductBlocks(numProductBlocks: Int): this.type = {
     this.numProductBlocks = numProductBlocks
     this
   }
 
   /** Set the rank of the feature matrices computed (number of features). Default: 10. */
+  @Since("0.8.0")
   def setRank(rank: Int): this.type = {
     this.rank = rank
     this
   }
 
   /** Set the number of iterations to run. Default: 10. */
+  @Since("0.8.0")
   def setIterations(iterations: Int): this.type = {
     this.iterations = iterations
     this
   }
 
   /** Set the regularization parameter, lambda. Default: 0.01. */
+  @Since("0.8.0")
   def setLambda(lambda: Double): this.type = {
     this.lambda = lambda
     this
   }
 
   /** Sets whether to use implicit preference. Default: false. */
+  @Since("0.8.1")
   def setImplicitPrefs(implicitPrefs: Boolean): this.type = {
     this.implicitPrefs = implicitPrefs
     this
@@ -139,12 +151,14 @@ class ALS private (
   /**
    * Sets the constant used in computing confidence in implicit ALS. Default: 1.0.
    */
+  @Since("0.8.1")
   def setAlpha(alpha: Double): this.type = {
     this.alpha = alpha
     this
   }
 
   /** Sets a random seed to have deterministic results. */
+  @Since("1.0.0")
   def setSeed(seed: Long): this.type = {
     this.seed = seed
     this
@@ -154,6 +168,7 @@ class ALS private (
    * Set whether the least-squares problems solved at each iteration should have
    * nonnegativity constraints.
    */
+  @Since("1.1.0")
   def setNonnegative(b: Boolean): this.type = {
     this.nonnegative = b
     this
@@ -166,6 +181,7 @@ class ALS private (
    * set `spark.rdd.compress` to `true` to reduce the space requirement, at the cost of speed.
    */
   @DeveloperApi
+  @Since("1.1.0")
   def setIntermediateRDDStorageLevel(storageLevel: StorageLevel): this.type = {
     require(storageLevel != StorageLevel.NONE,
       "ALS is not designed to run without persisting intermediate RDDs.")
@@ -181,6 +197,7 @@ class ALS private (
    * at the cost of speed.
    */
   @DeveloperApi
+  @Since("1.3.0")
   def setFinalRDDStorageLevel(storageLevel: StorageLevel): this.type = {
     this.finalRDDStorageLevel = storageLevel
     this
@@ -194,6 +211,7 @@ class ALS private (
    * this setting is ignored.
    */
   @DeveloperApi
+  @Since("1.4.0")
   def setCheckpointInterval(checkpointInterval: Int): this.type = {
     this.checkpointInterval = checkpointInterval
     this
@@ -203,6 +221,7 @@ class ALS private (
    * Run ALS with the configured parameters on an input RDD of (user, product, rating) triples.
    * Returns a MatrixFactorizationModel with feature vectors for each user and product.
    */
+  @Since("0.8.0")
   def run(ratings: RDD[Rating]): MatrixFactorizationModel = {
     val sc = ratings.context
 
@@ -250,6 +269,7 @@ class ALS private (
   /**
    * Java-friendly version of [[ALS.run]].
    */
+  @Since("1.3.0")
   def run(ratings: JavaRDD[Rating]): MatrixFactorizationModel = run(ratings.rdd)
 }
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
index ba4cfdcd9f..46562eb2ad 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
@@ -52,10 +52,10 @@ import org.apache.spark.storage.StorageLevel
  *                        and the features computed for this product.
  */
 @Since("0.8.0")
-class MatrixFactorizationModel(
-    val rank: Int,
-    val userFeatures: RDD[(Int, Array[Double])],
-    val productFeatures: RDD[(Int, Array[Double])])
+class MatrixFactorizationModel @Since("0.8.0") (
+    @Since("0.8.0") val rank: Int,
+    @Since("0.8.0") val userFeatures: RDD[(Int, Array[Double])],
+    @Since("0.8.0") val productFeatures: RDD[(Int, Array[Double])])
   extends Saveable with Serializable with Logging {
 
   require(rank > 0)