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 1b45eafbaca2340bbe3508bd30381fba79c492e2..aaaf7df34576a7f53c157e6c07cdbbd586ed3f1a 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],
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 e58df6ba9108ab53480679efe72dbdbe6c57555d..4c20e6563bad17804392dc5ce39de83c85c1d82a 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 {
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 c764c3aa32a4c3b38ac1d03438b35fb775cddee7..ac56845581ae9800705561087bb8c36b2d12c5d4 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 {
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 ad4f79a79c90f2b13010b4cb2ad3a4a1114ef496..e168a418cbb4247b046d099600cc21ad2355d03f 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 {
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 cd403d842b694c7ab8a699d767c6b397eb425720..583e5e0928eba8c5666e279f2b51adba0a8a80c4 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 {
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 7d8e4adcc2259f9abdd7f75a76ed5746d364c67e..c5d0ec1a8d350b4f342425cd24263b86c68e4913 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 = {
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 acabf0b892660e6445e24fced3e0431fffbea074..85f9732f79f6729305a8e38e085fe6f8af69b394 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,
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 95f480455ee451cb938523c107a28f4bb4780569..c0e3801499818b10c38d3cb1d2daa137a7391ccc 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] {
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 96b49bcc0aac14e3f3caea8cf9108dbf5932c06b..48bae4276c480e3d43362942f419e62387016722 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:
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 8054a34db30f29d10131e73c86f29408bd006180..5fe4bab186bd2ac017170b43b910a2deab093799 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
diff --git a/python/pyspark/ml/clustering.py b/python/pyspark/ml/clustering.py
index b29b5ac70e6fef46b01ac6ba82c35a5a5900459d..7f8d8455647685ee28f466a720ef090832a2ce6f 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:
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 40b63d4d31d4bbc2f13df72c0ec3432cd3faad23..aada38d1ad2ecae342b2eb2798378d6f70780032 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
diff --git a/python/pyspark/ml/util.py b/python/pyspark/ml/util.py
index bec4b289521023e10553343e5d194c6c28ed2cde..c65b3d14be1dfb8a395bc0907fdd97ae316109ae 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