[SPARK-16127][ML][PYPSARK] Audit @Since annotations related to ml.linalg

mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala

@@ -483,7 +483,7 @@ object LogisticRegression extends DefaultParamsReadable[LogisticRegression] {
 @Experimental
 class LogisticRegressionModel private[spark] (
     @Since("1.4.0") override val uid: String,
-    @Since("1.6.0") val coefficients: Vector,
+    @Since("2.0.0") val coefficients: Vector,
     @Since("1.3.0") val intercept: Double)
   extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel]
   with LogisticRegressionParams with MLWritable {

mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala

@@ -296,7 +296,7 @@ object MultilayerPerceptronClassifier
 class MultilayerPerceptronClassificationModel private[ml] (
     @Since("1.5.0") override val uid: String,
     @Since("1.5.0") val layers: Array[Int],
-    @Since("1.5.0") val weights: Vector)
+    @Since("2.0.0") val weights: Vector)
   extends PredictionModel[Vector, MultilayerPerceptronClassificationModel]
   with Serializable with MLWritable {
 

mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala

@@ -130,8 +130,8 @@ object NaiveBayes extends DefaultParamsReadable[NaiveBayes] {
 @Experimental
 class NaiveBayesModel private[ml] (
     @Since("1.5.0") override val uid: String,
-    @Since("1.5.0") val pi: Vector,
-    @Since("1.5.0") val theta: Matrix)
+    @Since("2.0.0") val pi: Vector,
+    @Since("2.0.0") val theta: Matrix)
   extends ProbabilisticClassificationModel[Vector, NaiveBayesModel]
   with NaiveBayesParams with MLWritable {
 

mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala

@@ -131,7 +131,7 @@ class KMeansModel private[ml] (
 
   private[clustering] def predict(features: Vector): Int = parentModel.predict(features)
 
-  @Since("1.5.0")
+  @Since("2.0.0")
   def clusterCenters: Array[Vector] = parentModel.clusterCenters.map(_.asML)
 
   /**

mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala

@@ -432,7 +432,7 @@ sealed abstract class LDAModel private[ml] (
    * If Online LDA was used and [[optimizeDocConcentration]] was set to false,
    * then this returns the fixed (given) value for the [[docConcentration]] parameter.
    */
-  @Since("1.6.0")
+  @Since("2.0.0")
   def estimatedDocConcentration: Vector = getModel.docConcentration
 
   /**
@@ -444,7 +444,7 @@ sealed abstract class LDAModel private[ml] (
    *          the Expectation-Maximization ("em") [[optimizer]], then this method could involve
    *          collecting a large amount of data to the driver (on the order of vocabSize x k).
    */
-  @Since("1.6.0")
+  @Since("2.0.0")
   def topicsMatrix: Matrix = oldLocalModel.topicsMatrix.asML
 
   /** Indicates whether this instance is of type [[DistributedLDAModel]] */

mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala

@@ -33,11 +33,11 @@ import org.apache.spark.sql.types.DataType
  * multiplier.
  */
 @Experimental
-@Since("2.0.0")
-class ElementwiseProduct @Since("2.0.0") (@Since("2.0.0") override val uid: String)
+@Since("1.4.0")
+class ElementwiseProduct @Since("1.4.0") (@Since("1.4.0") override val uid: String)
   extends UnaryTransformer[Vector, Vector, ElementwiseProduct] with DefaultParamsWritable {
 
-  @Since("2.0.0")
+  @Since("1.4.0")
   def this() = this(Identifiable.randomUID("elemProd"))
 
   /**

mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala

@@ -31,29 +31,29 @@ import org.apache.spark.sql.types.DataType
  * Normalize a vector to have unit norm using the given p-norm.
  */
 @Experimental
-@Since("2.0.0")
-class Normalizer @Since("2.0.0") (@Since("2.0.0") override val uid: String)
+@Since("1.4.0")
+class Normalizer @Since("1.4.0") (@Since("1.4.0") override val uid: String)
   extends UnaryTransformer[Vector, Vector, Normalizer] with DefaultParamsWritable {
 
-  @Since("2.0.0")
+  @Since("1.4.0")
   def this() = this(Identifiable.randomUID("normalizer"))
 
   /**
    * Normalization in L^p^ space.  Must be >= 1.
    * (default: p = 2)
    * @group param
    */
-  @Since("2.0.0")
+  @Since("1.4.0")
   val p = new DoubleParam(this, "p", "the p norm value", ParamValidators.gtEq(1))
 
   setDefault(p -> 2.0)
 
   /** @group getParam */
-  @Since("2.0.0")
+  @Since("1.4.0")
   def getP: Double = $(p)
 
   /** @group setParam */
-  @Since("2.0.0")
+  @Since("1.4.0")
   def setP(value: Double): this.type = set(p, value)
 
   override protected def createTransformFunc: Vector => Vector = {

mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala

@@ -35,30 +35,30 @@ import org.apache.spark.sql.types.DataType
  * `(x, y)`, if we want to expand it with degree 2, then we get `(x, x * x, y, x * y, y * y)`.
  */
 @Experimental
-@Since("2.0.0")
-class PolynomialExpansion @Since("2.0.0") (@Since("2.0.0") override val uid: String)
+@Since("1.4.0")
+class PolynomialExpansion @Since("1.4.0") (@Since("1.4.0") override val uid: String)
   extends UnaryTransformer[Vector, Vector, PolynomialExpansion] with DefaultParamsWritable {
 
-  @Since("2.0.0")
+  @Since("1.4.0")
   def this() = this(Identifiable.randomUID("poly"))
 
   /**
    * The polynomial degree to expand, which should be >= 1.  A value of 1 means no expansion.
    * Default: 2
    * @group param
    */
-  @Since("2.0.0")
+  @Since("1.4.0")
   val degree = new IntParam(this, "degree", "the polynomial degree to expand (>= 1)",
     ParamValidators.gtEq(1))
 
   setDefault(degree -> 2)
 
   /** @group getParam */
-  @Since("2.0.0")
+  @Since("1.4.0")
   def getDegree: Int = $(degree)
 
   /** @group setParam */
-  @Since("2.0.0")
+  @Since("1.4.0")
   def setDegree(value: Int): this.type = set(degree, value)
 
   override protected def createTransformFunc: Vector => Vector = { v =>

mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala

@@ -240,7 +240,7 @@ class Word2VecModel private[ml] (
    * of the word. Returns a dataframe with the words and the cosine similarities between the
    * synonyms and the given word vector.
    */
-  @Since("1.5.0")
+  @Since("2.0.0")
   def findSynonyms(word: Vector, num: Int): DataFrame = {
     val spark = SparkSession.builder().getOrCreate()
     spark.createDataFrame(wordVectors.findSynonyms(word, num)).toDF("word", "similarity")

mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala

@@ -286,7 +286,7 @@ object AFTSurvivalRegression extends DefaultParamsReadable[AFTSurvivalRegression
 @Since("1.6.0")
 class AFTSurvivalRegressionModel private[ml] (
     @Since("1.6.0") override val uid: String,
-    @Since("1.6.0") val coefficients: Vector,
+    @Since("2.0.0") val coefficients: Vector,
     @Since("1.6.0") val intercept: Double,
     @Since("1.6.0") val scale: Double)
   extends Model[AFTSurvivalRegressionModel] with AFTSurvivalRegressionParams with MLWritable {
@@ -307,7 +307,7 @@ class AFTSurvivalRegressionModel private[ml] (
   @Since("1.6.0")
   def setQuantilesCol(value: String): this.type = set(quantilesCol, value)
 
-  @Since("1.6.0")
+  @Since("2.0.0")
   def predictQuantiles(features: Vector): Vector = {
     // scale parameter for the Weibull distribution of lifetime
     val lambda = math.exp(BLAS.dot(coefficients, features) + intercept)
@@ -319,7 +319,7 @@ class AFTSurvivalRegressionModel private[ml] (
     Vectors.dense(quantiles)
   }
 
-  @Since("1.6.0")
+  @Since("2.0.0")
   def predict(features: Vector): Double = {
     math.exp(BLAS.dot(coefficients, features) + intercept)
   }

mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala

@@ -221,14 +221,14 @@ class IsotonicRegressionModel private[ml] (
   def setFeatureIndex(value: Int): this.type = set(featureIndex, value)
 
   /** Boundaries in increasing order for which predictions are known. */
-  @Since("1.5.0")
+  @Since("2.0.0")
   def boundaries: Vector = Vectors.dense(oldModel.boundaries)
 
   /**
    * Predictions associated with the boundaries at the same index, monotone because of isotonic
    * regression.
    */
-  @Since("1.5.0")
+  @Since("2.0.0")
   def predictions: Vector = Vectors.dense(oldModel.predictions)
 
   @Since("1.5.0")

mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala

@@ -387,9 +387,9 @@ object LinearRegression extends DefaultParamsReadable[LinearRegression] {
 @Since("1.3.0")
 @Experimental
 class LinearRegressionModel private[ml] (
-    override val uid: String,
-    val coefficients: Vector,
-    val intercept: Double)
+    @Since("1.4.0") override val uid: String,
+    @Since("2.0.0") val coefficients: Vector,
+    @Since("1.3.0") val intercept: Double)
   extends RegressionModel[Vector, LinearRegressionModel]
   with LinearRegressionParams with MLWritable {
 

python/pyspark/ml/classification.py

@@ -214,7 +214,7 @@ class LogisticRegressionModel(JavaModel, JavaMLWritable, JavaMLReadable):
     """
 
     @property
-    @since("1.6.0")
+    @since("2.0.0")
     def coefficients(self):
         """
         Model coefficients.
@@ -1025,15 +1025,15 @@ class NaiveBayesModel(JavaModel, JavaMLWritable, JavaMLReadable):
     """
 
     @property
-    @since("1.5.0")
+    @since("2.0.0")
     def pi(self):
         """
         log of class priors.
         """
         return self._call_java("pi")
 
     @property
-    @since("1.5.0")
+    @since("2.0.0")
     def theta(self):
         """
         log of class conditional probabilities.
@@ -1230,7 +1230,7 @@ def layers(self):
         return self._call_java("javaLayers")
 
     @property
-    @since("1.6.0")
+    @since("2.0.0")
     def weights(self):
         """
         vector of initial weights for the model that consists of the weights of layers.

python/pyspark/ml/regression.py

@@ -138,7 +138,7 @@ class LinearRegressionModel(JavaModel, JavaMLWritable, JavaMLReadable):
     """
 
     @property
-    @since("1.6.0")
+    @since("2.0.0")
     def coefficients(self):
         """
         Model coefficients.
@@ -511,13 +511,15 @@ class IsotonicRegressionModel(JavaModel, JavaMLWritable, JavaMLReadable):
     """
 
     @property
+    @since("2.0.0")
     def boundaries(self):
         """
         Boundaries in increasing order for which predictions are known.
         """
         return self._call_java("boundaries")
 
     @property
+    @since("2.0.0")
     def predictions(self):
         """
         Predictions associated with the boundaries at the same index, monotone because of isotonic
@@ -1248,7 +1250,7 @@ class AFTSurvivalRegressionModel(JavaModel, JavaMLWritable, JavaMLReadable):
     """
 
     @property
-    @since("1.6.0")
+    @since("2.0.0")
     def coefficients(self):
         """
         Model coefficients.
@@ -1271,12 +1273,14 @@ def scale(self):
         """
         return self._call_java("scale")
 
+    @since("2.0.0")
     def predictQuantiles(self, features):
         """
         Predicted Quantiles
         """
         return self._call_java("predictQuantiles", features)
 
+    @since("2.0.0")
     def predict(self, features):
         """
         Predicted value