add Experimental annotation

Author: mengxr

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

@@ -19,6 +19,7 @@ package org.apache.spark.mllib.classification
 
 import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum}
 
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.{Logging, SparkContext}
 import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.linalg.Vector
@@ -36,6 +37,7 @@ import org.apache.spark.rdd.RDD
  * @param theta log of class conditional probabilities, whose dimension is C-by-D,
  *              where D is number of features
  */
+@Experimental
 class NaiveBayesModel(
     val labels: Array[Double],
     val pi: Array[Double],

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

@@ -21,6 +21,7 @@ import scala.collection.mutable.ArrayBuffer
 
 import breeze.linalg.{DenseVector => BDV, Vector => BV, norm => breezeNorm}
 
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.{Logging, SparkContext}
 import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
@@ -400,6 +401,7 @@ object KMeans {
   /**
    * :: Experimental ::
    */
+  @Experimental
   def main(args: Array[String]) {
     if (args.length < 4) {
       println("Usage: KMeans <master> <input_file> <k> <max_iterations> [<runs>]")

mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala

@@ -25,6 +25,7 @@ import scala.util.Sorting
 import com.esotericsoftware.kryo.Kryo
 import org.jblas.{DoubleMatrix, SimpleBlas, Solve}
 
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.{Logging, HashPartitioner, Partitioner, SparkContext, SparkConf}
 import org.apache.spark.storage.StorageLevel
@@ -142,6 +143,7 @@ class ALS private (
    *
    * Sets the constant used in computing confidence in implicit ALS. Default: 1.0.
    */
+  @Experimental
   def setAlpha(alpha: Double): ALS = {
     this.alpha = alpha
     this

mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala

@@ -19,6 +19,7 @@ package org.apache.spark.mllib.regression
 
 import breeze.linalg.{DenseVector => BDV, SparseVector => BSV}
 
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.optimization._
@@ -105,6 +106,7 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
    *
    * Set if the algorithm should validate data before training. Default true.
    */
+  @Experimental
   def setValidateData(validateData: Boolean): this.type = {
     this.validateData = validateData
     this

mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala

@@ -19,6 +19,7 @@ package org.apache.spark.mllib.tree
 
 import scala.util.control.Breaks._
 
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.{Logging, SparkContext}
 import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -41,6 +42,7 @@ import org.apache.spark.mllib.linalg.{Vector, Vectors}
  *                 of algorithm (classification, regression, etc.), feature type (continuous,
  *                 categorical), depth of the tree, quantile calculation strategy, etc.
  */
+@Experimental
 class DecisionTree (private val strategy: Strategy) extends Serializable with Logging {
 
   /**

mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala

@@ -17,11 +17,14 @@
 
 package org.apache.spark.mllib.tree.configuration
 
+import org.apache.spark.annotation.Experimental
+
 /**
  * :: Experimental ::
  *
  * Enum to select the algorithm for the decision tree
  */
+@Experimental
 object Algo extends Enumeration {
   type Algo = Value
   val Classification, Regression = Value

mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala

@@ -17,11 +17,14 @@
 
 package org.apache.spark.mllib.tree.configuration
 
+import org.apache.spark.annotation.Experimental
+
 /**
  * :: Experimental ::
  *
  * Enum to describe whether a feature is "continuous" or "categorical"
  */
+@Experimental
 object FeatureType extends Enumeration {
   type FeatureType = Value
   val Continuous, Categorical = Value

mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala

@@ -17,11 +17,14 @@
 
 package org.apache.spark.mllib.tree.configuration
 
+import org.apache.spark.annotation.Experimental
+
 /**
  * :: Experimental ::
  *
  * Enum for selecting the quantile calculation strategy
  */
+@Experimental
 object QuantileStrategy extends Enumeration {
   type QuantileStrategy = Value
   val Sort, MinMax, ApproxHist = Value

mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala

@@ -17,6 +17,7 @@
 
 package org.apache.spark.mllib.tree.configuration
 
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.mllib.tree.impurity.Impurity
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
@@ -36,6 +37,7 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
  *                                1, 2, ... , k-1. It's important to note that features are
  *                                zero-indexed.
  */
+@Experimental
 class Strategy (
     val algo: Algo,
     val impurity: Impurity,

mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala

@@ -17,14 +17,15 @@
 
 package org.apache.spark.mllib.tree.impurity
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Experimental}
 
 /**
  * :: Experimental ::
  *
  * Class for calculating [[http://en.wikipedia.org/wiki/Binary_entropy_function entropy]] during
  * binary classification.
  */
+@Experimental
 object Entropy extends Impurity {
 
   private[tree] def log2(x: Double) = scala.math.log(x) / scala.math.log(2)