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From jkbrad...@apache.org
Subject spark git commit: [SPARK-18319][ML][QA2.1] 2.1 QA: API: Experimental, DeveloperApi, final, sealed audit
Date Wed, 30 Nov 2016 02:47:05 GMT
Repository: spark
Updated Branches:
  refs/heads/master c3d08e2f2 -> 9b670bcae


[SPARK-18319][ML][QA2.1] 2.1 QA: API: Experimental, DeveloperApi, final, sealed audit

## What changes were proposed in this pull request?
make a pass through the items marked as Experimental or DeveloperApi and see if any are stable
enough to be unmarked. Also check for items marked final or sealed to see if they are stable
enough to be opened up as APIs.

Some discussions in the jira: https://issues.apache.org/jira/browse/SPARK-18319

## How was this patch tested?
existing ut

Author: Yuhao <yuhao.yang@intel.com>
Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #15972 from hhbyyh/experimental21.


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/9b670bca
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/9b670bca
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/9b670bca

Branch: refs/heads/master
Commit: 9b670bcaec9c220603ec10a6d186865dabf26a5b
Parents: c3d08e2
Author: Yuhao <yuhao.yang@intel.com>
Authored: Tue Nov 29 18:46:59 2016 -0800
Committer: Joseph K. Bradley <joseph@databricks.com>
Committed: Tue Nov 29 18:46:59 2016 -0800

----------------------------------------------------------------------
 .../MultilayerPerceptronClassifier.scala            |  6 +-----
 .../spark/ml/clustering/BisectingKMeans.scala       |  5 -----
 .../spark/ml/clustering/GaussianMixture.scala       |  5 -----
 .../org/apache/spark/ml/clustering/KMeans.scala     |  4 ----
 .../scala/org/apache/spark/ml/clustering/LDA.scala  | 12 ++----------
 .../org/apache/spark/ml/feature/LabeledPoint.scala  |  4 +---
 .../org/apache/spark/ml/feature/MaxAbsScaler.scala  |  6 +-----
 .../scala/org/apache/spark/ml/util/ReadWrite.scala  | 14 +-------------
 .../spark/mllib/clustering/LDAOptimizer.scala       |  2 +-
 python/pyspark/ml/classification.py                 |  4 ----
 python/pyspark/ml/clustering.py                     | 16 ----------------
 python/pyspark/ml/feature.py                        |  4 ----
 python/pyspark/ml/util.py                           |  8 --------
 13 files changed, 7 insertions(+), 83 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/spark/blob/9b670bca/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
index 1b45eaf..aaaf7df 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
@@ -21,7 +21,7 @@ import scala.collection.JavaConverters._
 
 import org.apache.hadoop.fs.Path
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.ml.{PredictionModel, Predictor, PredictorParams}
 import org.apache.spark.ml.ann.{FeedForwardTopology, FeedForwardTrainer}
 import org.apache.spark.ml.feature.LabeledPoint
@@ -135,7 +135,6 @@ private object LabelConverter {
 }
 
 /**
- * :: Experimental ::
  * Classifier trainer based on the Multilayer Perceptron.
  * Each layer has sigmoid activation function, output layer has softmax.
  * Number of inputs has to be equal to the size of feature vectors.
@@ -143,7 +142,6 @@ private object LabelConverter {
  *
  */
 @Since("1.5.0")
-@Experimental
 class MultilayerPerceptronClassifier @Since("1.5.0") (
     @Since("1.5.0") override val uid: String)
   extends Predictor[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel]
@@ -282,7 +280,6 @@ object MultilayerPerceptronClassifier
 }
 
 /**
- * :: Experimental ::
  * Classification model based on the Multilayer Perceptron.
  * Each layer has sigmoid activation function, output layer has softmax.
  *
@@ -291,7 +288,6 @@ object MultilayerPerceptronClassifier
  * @param weights the weights of layers
  */
 @Since("1.5.0")
-@Experimental
 class MultilayerPerceptronClassificationModel private[ml] (
     @Since("1.5.0") override val uid: String,
     @Since("1.5.0") val layers: Array[Int],

http://git-wip-us.apache.org/repos/asf/spark/blob/9b670bca/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala
index e58df6b..4c20e65 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala
@@ -80,13 +80,11 @@ private[clustering] trait BisectingKMeansParams extends Params
 }
 
 /**
- * :: Experimental ::
  * Model fitted by BisectingKMeans.
  *
  * @param parentModel a model trained by [[org.apache.spark.mllib.clustering.BisectingKMeans]].
  */
 @Since("2.0.0")
-@Experimental
 class BisectingKMeansModel private[ml] (
     @Since("2.0.0") override val uid: String,
     private val parentModel: MLlibBisectingKMeansModel
@@ -197,8 +195,6 @@ object BisectingKMeansModel extends MLReadable[BisectingKMeansModel] {
 }
 
 /**
- * :: Experimental ::
- *
  * A bisecting k-means algorithm based on the paper "A comparison of document clustering
techniques"
  * by Steinbach, Karypis, and Kumar, with modification to fit Spark.
  * The algorithm starts from a single cluster that contains all points.
@@ -213,7 +209,6 @@ object BisectingKMeansModel extends MLReadable[BisectingKMeansModel] {
  * KDD Workshop on Text Mining, 2000.</a>
  */
 @Since("2.0.0")
-@Experimental
 class BisectingKMeans @Since("2.0.0") (
     @Since("2.0.0") override val uid: String)
   extends Estimator[BisectingKMeansModel] with BisectingKMeansParams with DefaultParamsWritable
{

http://git-wip-us.apache.org/repos/asf/spark/blob/9b670bca/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala
index c764c3a..ac56845 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala
@@ -68,8 +68,6 @@ private[clustering] trait GaussianMixtureParams extends Params with HasMaxIter
w
 }
 
 /**
- * :: Experimental ::
- *
  * Multivariate Gaussian Mixture Model (GMM) consisting of k Gaussians, where points
  * are drawn from each Gaussian i with probability weights(i).
  *
@@ -80,7 +78,6 @@ private[clustering] trait GaussianMixtureParams extends Params with HasMaxIter
w
  *                  the Multivariate Gaussian (Normal) Distribution for Gaussian i
  */
 @Since("2.0.0")
-@Experimental
 class GaussianMixtureModel private[ml] (
     @Since("2.0.0") override val uid: String,
     @Since("2.0.0") val weights: Array[Double],
@@ -265,7 +262,6 @@ object GaussianMixtureModel extends MLReadable[GaussianMixtureModel] {
 }
 
 /**
- * :: Experimental ::
  * Gaussian Mixture clustering.
  *
  * This class performs expectation maximization for multivariate Gaussian
@@ -284,7 +280,6 @@ object GaussianMixtureModel extends MLReadable[GaussianMixtureModel] {
  * on statistical/theoretical arguments) and (b) numerical issues with Gaussian distributions.
  */
 @Since("2.0.0")
-@Experimental
 class GaussianMixture @Since("2.0.0") (
     @Since("2.0.0") override val uid: String)
   extends Estimator[GaussianMixtureModel] with GaussianMixtureParams with DefaultParamsWritable
{

http://git-wip-us.apache.org/repos/asf/spark/blob/9b670bca/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
index ad4f79a..e168a41 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
@@ -96,13 +96,11 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter
with HasFe
 }
 
 /**
- * :: Experimental ::
  * Model fitted by KMeans.
  *
  * @param parentModel a model trained by spark.mllib.clustering.KMeans.
  */
 @Since("1.5.0")
-@Experimental
 class KMeansModel private[ml] (
     @Since("1.5.0") override val uid: String,
     private val parentModel: MLlibKMeansModel)
@@ -248,13 +246,11 @@ object KMeansModel extends MLReadable[KMeansModel] {
 }
 
 /**
- * :: Experimental ::
  * K-means clustering with support for k-means|| initialization proposed by Bahmani et al.
  *
  * @see <a href="http://dx.doi.org/10.14778/2180912.2180915">Bahmani et al., Scalable
k-means++.</a>
  */
 @Since("1.5.0")
-@Experimental
 class KMeans @Since("1.5.0") (
     @Since("1.5.0") override val uid: String)
   extends Estimator[KMeansModel] with KMeansParams with DefaultParamsWritable {

http://git-wip-us.apache.org/repos/asf/spark/blob/9b670bca/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala
index cd403d8..583e5e0 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala
@@ -22,7 +22,7 @@ import org.json4s.DefaultFormats
 import org.json4s.JsonAST.JObject
 import org.json4s.jackson.JsonMethods._
 
-import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.linalg.{Matrix, Vector, Vectors, VectorUDT}
@@ -396,15 +396,13 @@ private object LDAParams {
 
 
 /**
- * :: Experimental ::
  * Model fitted by [[LDA]].
  *
  * @param vocabSize  Vocabulary size (number of terms or words in the vocabulary)
  * @param sparkSession  Used to construct local DataFrames for returning query results
  */
 @Since("1.6.0")
-@Experimental
-sealed abstract class LDAModel private[ml] (
+abstract class LDAModel private[ml] (
     @Since("1.6.0") override val uid: String,
     @Since("1.6.0") val vocabSize: Int,
     @Since("1.6.0") @transient private[ml] val sparkSession: SparkSession)
@@ -556,14 +554,12 @@ sealed abstract class LDAModel private[ml] (
 
 
 /**
- * :: Experimental ::
  *
  * Local (non-distributed) model fitted by [[LDA]].
  *
  * This model stores the inferred topics only; it does not store info about the training
dataset.
  */
 @Since("1.6.0")
-@Experimental
 class LocalLDAModel private[ml] (
     uid: String,
     vocabSize: Int,
@@ -641,7 +637,6 @@ object LocalLDAModel extends MLReadable[LocalLDAModel] {
 
 
 /**
- * :: Experimental ::
  *
  * Distributed model fitted by [[LDA]].
  * This type of model is currently only produced by Expectation-Maximization (EM).
@@ -653,7 +648,6 @@ object LocalLDAModel extends MLReadable[LocalLDAModel] {
  *                             `copy()` cheap.
  */
 @Since("1.6.0")
-@Experimental
 class DistributedLDAModel private[ml] (
     uid: String,
     vocabSize: Int,
@@ -789,7 +783,6 @@ object DistributedLDAModel extends MLReadable[DistributedLDAModel] {
 
 
 /**
- * :: Experimental ::
  *
  * Latent Dirichlet Allocation (LDA), a topic model designed for text documents.
  *
@@ -813,7 +806,6 @@ object DistributedLDAModel extends MLReadable[DistributedLDAModel] {
  * Latent Dirichlet allocation (Wikipedia)</a>
  */
 @Since("1.6.0")
-@Experimental
 class LDA @Since("1.6.0") (
     @Since("1.6.0") override val uid: String)
   extends Estimator[LDAModel] with LDAParams with DefaultParamsWritable {

http://git-wip-us.apache.org/repos/asf/spark/blob/9b670bca/mllib/src/main/scala/org/apache/spark/ml/feature/LabeledPoint.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/LabeledPoint.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/LabeledPoint.scala
index 7d8e4ad..c5d0ec1 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/LabeledPoint.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/LabeledPoint.scala
@@ -19,11 +19,10 @@ package org.apache.spark.ml.feature
 
 import scala.beans.BeanInfo
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.ml.linalg.Vector
 
 /**
- * :: Experimental ::
  *
  * Class that represents the features and label of a data point.
  *
@@ -31,7 +30,6 @@ import org.apache.spark.ml.linalg.Vector
  * @param features List of features for this data point.
  */
 @Since("2.0.0")
-@Experimental
 @BeanInfo
 case class LabeledPoint(@Since("2.0.0") label: Double, @Since("2.0.0") features: Vector)
{
   override def toString: String = {

http://git-wip-us.apache.org/repos/asf/spark/blob/9b670bca/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala
index acabf0b..85f9732 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala
@@ -19,7 +19,7 @@ package org.apache.spark.ml.feature
 
 import org.apache.hadoop.fs.Path
 
-import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
 import org.apache.spark.ml.param.{ParamMap, Params}
@@ -48,12 +48,10 @@ private[feature] trait MaxAbsScalerParams extends Params with HasInputCol
with H
 }
 
 /**
- * :: Experimental ::
  * Rescale each feature individually to range [-1, 1] by dividing through the largest maximum
  * absolute value in each feature. It does not shift/center the data, and thus does not destroy
  * any sparsity.
  */
-@Experimental
 @Since("2.0.0")
 class MaxAbsScaler @Since("2.0.0") (@Since("2.0.0") override val uid: String)
   extends Estimator[MaxAbsScalerModel] with MaxAbsScalerParams with DefaultParamsWritable
{
@@ -101,11 +99,9 @@ object MaxAbsScaler extends DefaultParamsReadable[MaxAbsScaler] {
 }
 
 /**
- * :: Experimental ::
  * Model fitted by [[MaxAbsScaler]].
  *
  */
-@Experimental
 @Since("2.0.0")
 class MaxAbsScalerModel private[ml] (
     @Since("2.0.0") override val uid: String,

http://git-wip-us.apache.org/repos/asf/spark/blob/9b670bca/mllib/src/main/scala/org/apache/spark/ml/util/ReadWrite.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/ReadWrite.scala b/mllib/src/main/scala/org/apache/spark/ml/util/ReadWrite.scala
index 95f4804..c0e3801 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/util/ReadWrite.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/util/ReadWrite.scala
@@ -26,7 +26,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml._
 import org.apache.spark.ml.classification.{OneVsRest, OneVsRestModel}
@@ -81,11 +81,8 @@ private[util] sealed trait BaseReadWrite {
 }
 
 /**
- * :: Experimental ::
- *
  * Abstract class for utility classes that can save ML instances.
  */
-@Experimental
 @Since("1.6.0")
 abstract class MLWriter extends BaseReadWrite with Logging {
 
@@ -138,11 +135,8 @@ abstract class MLWriter extends BaseReadWrite with Logging {
 }
 
 /**
- * :: Experimental ::
- *
  * Trait for classes that provide [[MLWriter]].
  */
-@Experimental
 @Since("1.6.0")
 trait MLWritable {
 
@@ -178,13 +172,10 @@ trait DefaultParamsWritable extends MLWritable { self: Params =>
 }
 
 /**
- * :: Experimental ::
- *
  * Abstract class for utility classes that can load ML instances.
  *
  * @tparam T ML instance type
  */
-@Experimental
 @Since("1.6.0")
 abstract class MLReader[T] extends BaseReadWrite {
 
@@ -202,13 +193,10 @@ abstract class MLReader[T] extends BaseReadWrite {
 }
 
 /**
- * :: Experimental ::
- *
  * Trait for objects that provide [[MLReader]].
  *
  * @tparam T ML instance type
  */
-@Experimental
 @Since("1.6.0")
 trait MLReadable[T] {
 

http://git-wip-us.apache.org/repos/asf/spark/blob/9b670bca/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index 96b49bc..48bae42 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -38,7 +38,7 @@ import org.apache.spark.storage.StorageLevel
  */
 @Since("1.4.0")
 @DeveloperApi
-sealed trait LDAOptimizer {
+trait LDAOptimizer {
 
   /*
     DEVELOPERS NOTE:

http://git-wip-us.apache.org/repos/asf/spark/blob/9b670bca/python/pyspark/ml/classification.py
----------------------------------------------------------------------
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 8054a34..5fe4bab 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -1138,8 +1138,6 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol,
HasLabelCol,
                                      HasMaxIter, HasTol, HasSeed, HasStepSize, JavaMLWritable,
                                      JavaMLReadable):
     """
-    .. note:: Experimental
-
     Classifier trainer based on the Multilayer Perceptron.
     Each layer has sigmoid activation function, output layer has softmax.
     Number of inputs has to be equal to the size of feature vectors.
@@ -1311,8 +1309,6 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol,
HasLabelCol,
 class MultilayerPerceptronClassificationModel(JavaModel, JavaPredictionModel, JavaMLWritable,
                                               JavaMLReadable):
     """
-    .. note:: Experimental
-
     Model fitted by MultilayerPerceptronClassifier.
 
     .. versionadded:: 1.6.0

http://git-wip-us.apache.org/repos/asf/spark/blob/9b670bca/python/pyspark/ml/clustering.py
----------------------------------------------------------------------
diff --git a/python/pyspark/ml/clustering.py b/python/pyspark/ml/clustering.py
index b29b5ac..7f8d845 100644
--- a/python/pyspark/ml/clustering.py
+++ b/python/pyspark/ml/clustering.py
@@ -87,8 +87,6 @@ class ClusteringSummary(JavaWrapper):
 
 class GaussianMixtureModel(JavaModel, JavaMLWritable, JavaMLReadable):
     """
-    .. note:: Experimental
-
     Model fitted by GaussianMixture.
 
     .. versionadded:: 2.0.0
@@ -141,8 +139,6 @@ class GaussianMixtureModel(JavaModel, JavaMLWritable, JavaMLReadable):
 class GaussianMixture(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasTol,
HasSeed,
                       HasProbabilityCol, JavaMLWritable, JavaMLReadable):
     """
-    .. note:: Experimental
-
     GaussianMixture clustering.
     This class performs expectation maximization for multivariate Gaussian
     Mixture Models (GMMs).  A GMM represents a composite distribution of
@@ -441,8 +437,6 @@ class KMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter,
HasTol
 
 class BisectingKMeansModel(JavaModel, JavaMLWritable, JavaMLReadable):
     """
-    .. note:: Experimental
-
     Model fitted by BisectingKMeans.
 
     .. versionadded:: 2.0.0
@@ -487,8 +481,6 @@ class BisectingKMeansModel(JavaModel, JavaMLWritable, JavaMLReadable):
 class BisectingKMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasSeed,
                       JavaMLWritable, JavaMLReadable):
     """
-    .. note:: Experimental
-
     A bisecting k-means algorithm based on the paper "A comparison of document clustering
     techniques" by Steinbach, Karypis, and Kumar, with modification to fit Spark.
     The algorithm starts from a single cluster that contains all points.
@@ -619,8 +611,6 @@ class BisectingKMeansSummary(ClusteringSummary):
 @inherit_doc
 class LDAModel(JavaModel):
     """
-    .. note:: Experimental
-
     Latent Dirichlet Allocation (LDA) model.
     This abstraction permits for different underlying representations,
     including local and distributed data structures.
@@ -697,8 +687,6 @@ class LDAModel(JavaModel):
 @inherit_doc
 class DistributedLDAModel(LDAModel, JavaMLReadable, JavaMLWritable):
     """
-    .. note:: Experimental
-
     Distributed model fitted by :py:class:`LDA`.
     This type of model is currently only produced by Expectation-Maximization (EM).
 
@@ -761,8 +749,6 @@ class DistributedLDAModel(LDAModel, JavaMLReadable, JavaMLWritable):
 @inherit_doc
 class LocalLDAModel(LDAModel, JavaMLReadable, JavaMLWritable):
     """
-    .. note:: Experimental
-
     Local (non-distributed) model fitted by :py:class:`LDA`.
     This model stores the inferred topics only; it does not store info about the training
dataset.
 
@@ -775,8 +761,6 @@ class LocalLDAModel(LDAModel, JavaMLReadable, JavaMLWritable):
 class LDA(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed, HasCheckpointInterval,
           JavaMLReadable, JavaMLWritable):
     """
-    .. note:: Experimental
-
     Latent Dirichlet Allocation (LDA), a topic model designed for text documents.
 
     Terminology:

http://git-wip-us.apache.org/repos/asf/spark/blob/9b670bca/python/pyspark/ml/feature.py
----------------------------------------------------------------------
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 40b63d4..aada38d 100755
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -654,8 +654,6 @@ class IDFModel(JavaModel, JavaMLReadable, JavaMLWritable):
 @inherit_doc
 class MaxAbsScaler(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
     """
-    .. note:: Experimental
-
     Rescale each feature individually to range [-1, 1] by dividing through the largest maximum
     absolute value in each feature. It does not shift/center the data, and thus does not
destroy
     any sparsity.
@@ -715,8 +713,6 @@ class MaxAbsScaler(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable,
Jav
 
 class MaxAbsScalerModel(JavaModel, JavaMLReadable, JavaMLWritable):
     """
-    .. note:: Experimental
-
     Model fitted by :py:class:`MaxAbsScaler`.
 
     .. versionadded:: 2.0.0

http://git-wip-us.apache.org/repos/asf/spark/blob/9b670bca/python/pyspark/ml/util.py
----------------------------------------------------------------------
diff --git a/python/pyspark/ml/util.py b/python/pyspark/ml/util.py
index bec4b28..c65b3d1 100644
--- a/python/pyspark/ml/util.py
+++ b/python/pyspark/ml/util.py
@@ -62,8 +62,6 @@ class Identifiable(object):
 @inherit_doc
 class MLWriter(object):
     """
-    .. note:: Experimental
-
     Utility class that can save ML instances.
 
     .. versionadded:: 2.0.0
@@ -129,8 +127,6 @@ class JavaMLWriter(MLWriter):
 @inherit_doc
 class MLWritable(object):
     """
-    .. note:: Experimental
-
     Mixin for ML instances that provide :py:class:`MLWriter`.
 
     .. versionadded:: 2.0.0
@@ -159,8 +155,6 @@ class JavaMLWritable(MLWritable):
 @inherit_doc
 class MLReader(object):
     """
-    .. note:: Experimental
-
     Utility class that can load ML instances.
 
     .. versionadded:: 2.0.0
@@ -242,8 +236,6 @@ class JavaMLReader(MLReader):
 @inherit_doc
 class MLReadable(object):
     """
-    .. note:: Experimental
-
     Mixin for instances that provide :py:class:`MLReader`.
 
     .. versionadded:: 2.0.0


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