[SPARK-30930][ML] Remove ML/MLLIB DeveloperApi annotations

### What changes were proposed in this pull request?
jira link: https://issues.apache.org/jira/browse/SPARK-30930

Remove ML/MLLIB DeveloperApi annotations.

### Why are the changes needed?

The Developer APIs in ML/MLLIB have been there for a long time. They are stable now and are very unlikely to be changed or removed, so I unmark these Developer APIs in this PR.

### Does this PR introduce any user-facing change?
Yes. DeveloperApi annotations are removed from docs.

### How was this patch tested?
existing tests

Closes #27859 from huaxingao/spark-30930.

Authored-by: Huaxin Gao <[email protected]>
Signed-off-by: Sean Owen <[email protected]>

Author: huaxingao

mllib/src/main/scala/org/apache/spark/ml/Estimator.scala

@@ -19,15 +19,13 @@ package org.apache.spark.ml
 
 import scala.annotation.varargs
 
-import org.apache.spark.annotation.{DeveloperApi, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.ml.param.{ParamMap, ParamPair}
 import org.apache.spark.sql.Dataset
 
 /**
- * :: DeveloperApi ::
  * Abstract class for estimators that fit models to data.
  */
-@DeveloperApi
 abstract class Estimator[M <: Model[M]] extends PipelineStage {
 
   /**

mllib/src/main/scala/org/apache/spark/ml/Model.scala

@@ -17,16 +17,13 @@
 
 package org.apache.spark.ml
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ml.param.ParamMap
 
 /**
- * :: DeveloperApi ::
  * A fitted model, i.e., a [[Transformer]] produced by an [[Estimator]].
  *
  * @tparam M model type
  */
-@DeveloperApi
 abstract class Model[M <: Model[M]] extends Transformer {
   /**
    * The parent estimator that produced this model.

mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala

@@ -36,14 +36,11 @@ import org.apache.spark.sql.{DataFrame, Dataset}
 import org.apache.spark.sql.types.StructType
 
 /**
- * :: DeveloperApi ::
  * A stage in a pipeline, either an [[Estimator]] or a [[Transformer]].
  */
-@DeveloperApi
 abstract class PipelineStage extends Params with Logging {
 
   /**
-   * :: DeveloperApi ::
    *
    * Check transform validity and derive the output schema from the input schema.
    *
@@ -54,7 +51,6 @@ abstract class PipelineStage extends Params with Logging {
    * Typical implementation should first conduct verification on schema change and parameter
    * validity, including complex parameter interaction checks.
    */
-  @DeveloperApi
   def transformSchema(schema: StructType): StructType
 
   /**

mllib/src/main/scala/org/apache/spark/ml/Predictor.scala

@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml
 
-import org.apache.spark.annotation.{DeveloperApi, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.ml.feature.{Instance, LabeledPoint}
 import org.apache.spark.ml.linalg.{Vector, VectorUDT}
 import org.apache.spark.ml.param._
@@ -99,7 +99,6 @@ private[ml] trait PredictorParams extends Params
 }
 
 /**
- * :: DeveloperApi ::
  * Abstraction for prediction problems (regression and classification). It accepts all NumericType
  * labels and will automatically cast it to DoubleType in `fit()`. If this predictor supports
  * weights, it accepts all NumericType weights, which will be automatically casted to DoubleType
@@ -112,7 +111,6 @@ private[ml] trait PredictorParams extends Params
  * @tparam M  Specialization of [[PredictionModel]].  If you subclass this type, use this type
  *            parameter to specify the concrete type for the corresponding model.
  */
-@DeveloperApi
 abstract class Predictor[
     FeaturesType,
     Learner <: Predictor[FeaturesType, Learner, M],
@@ -190,15 +188,13 @@ abstract class Predictor[
 }
 
 /**
- * :: DeveloperApi ::
  * Abstraction for a model for prediction tasks (regression and classification).
  *
  * @tparam FeaturesType  Type of features.
  *                       E.g., `VectorUDT` for vector features.
  * @tparam M  Specialization of [[PredictionModel]].  If you subclass this type, use this type
  *            parameter to specify the concrete type for the corresponding model.
  */
-@DeveloperApi
 abstract class PredictionModel[FeaturesType, M <: PredictionModel[FeaturesType, M]]
   extends Model[M] with PredictorParams {
 

mllib/src/main/scala/org/apache/spark/ml/Transformer.scala

@@ -20,7 +20,7 @@ package org.apache.spark.ml
 import scala.annotation.varargs
 import scala.reflect.runtime.universe.TypeTag
 
-import org.apache.spark.annotation.{DeveloperApi, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
@@ -29,10 +29,8 @@ import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
 
 /**
- * :: DeveloperApi ::
  * Abstract class for transformers that transform one dataset into another.
  */
-@DeveloperApi
 abstract class Transformer extends PipelineStage {
 
   /**
@@ -75,11 +73,9 @@ abstract class Transformer extends PipelineStage {
 }
 
 /**
- * :: DeveloperApi ::
  * Abstract class for transformers that take one input column, apply transformation, and output the
  * result as a new column.
  */
-@DeveloperApi
 abstract class UnaryTransformer[IN: TypeTag, OUT: TypeTag, T <: UnaryTransformer[IN, OUT, T]]
   extends Transformer with HasInputCol with HasOutputCol with Logging {
 

mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala

@@ -19,12 +19,10 @@ package org.apache.spark.ml.attribute
 
 import scala.collection.mutable.ArrayBuffer
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ml.linalg.VectorUDT
 import org.apache.spark.sql.types.{Metadata, MetadataBuilder, StructField}
 
 /**
- * :: DeveloperApi ::
  * Attributes that describe a vector ML column.
  *
  * @param name name of the attribute group (the ML column name)
@@ -33,7 +31,6 @@ import org.apache.spark.sql.types.{Metadata, MetadataBuilder, StructField}
  * @param attrs optional array of attributes. Attribute will be copied with their corresponding
  *              indices in the array.
  */
-@DeveloperApi
 class AttributeGroup private (
     val name: String,
     val numAttributes: Option[Int],
@@ -186,10 +183,8 @@ class AttributeGroup private (
 }
 
 /**
- * :: DeveloperApi ::
  * Factory methods to create attribute groups.
  */
-@DeveloperApi
 object AttributeGroup {
 
   import AttributeKeys._

mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeType.scala

@@ -17,20 +17,13 @@
 
 package org.apache.spark.ml.attribute
 
-import org.apache.spark.annotation.DeveloperApi
 
 /**
- * :: DeveloperApi ::
  * An enum-like type for attribute types: [[AttributeType$#Numeric]], [[AttributeType$#Nominal]],
  * and [[AttributeType$#Binary]].
  */
-@DeveloperApi
 sealed abstract class AttributeType(val name: String)
 
-/**
- * :: DeveloperApi ::
- */
-@DeveloperApi
 object AttributeType {
 
   /** Numeric type. */

mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala

@@ -19,14 +19,11 @@ package org.apache.spark.ml.attribute
 
 import scala.annotation.varargs
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.types.{DoubleType, Metadata, MetadataBuilder, NumericType, StructField}
 
 /**
- * :: DeveloperApi ::
  * Abstract class for ML attributes.
  */
-@DeveloperApi
 sealed abstract class Attribute extends Serializable {
 
   name.foreach { n =>
@@ -150,10 +147,6 @@ private[attribute] trait AttributeFactory {
   def fromStructField(field: StructField): Attribute = decodeStructField(field, false)
 }
 
-/**
- * :: DeveloperApi ::
- */
-@DeveloperApi
 object Attribute extends AttributeFactory {
 
   private[attribute] override def fromMetadata(metadata: Metadata): Attribute = {
@@ -182,7 +175,6 @@ object Attribute extends AttributeFactory {
 
 
 /**
- * :: DeveloperApi ::
  * A numeric attribute with optional summary statistics.
  * @param name optional name
  * @param index optional index
@@ -191,7 +183,6 @@ object Attribute extends AttributeFactory {
  * @param std optional standard deviation
  * @param sparsity optional sparsity (ratio of zeros)
  */
-@DeveloperApi
 class NumericAttribute private[ml] (
     override val name: Option[String] = None,
     override val index: Option[Int] = None,
@@ -299,10 +290,8 @@ class NumericAttribute private[ml] (
 }
 
 /**
- * :: DeveloperApi ::
  * Factory methods for numeric attributes.
  */
-@DeveloperApi
 object NumericAttribute extends AttributeFactory {
 
   /** The default numeric attribute. */
@@ -321,7 +310,6 @@ object NumericAttribute extends AttributeFactory {
 }
 
 /**
- * :: DeveloperApi ::
  * A nominal attribute.
  * @param name optional name
  * @param index optional index
@@ -330,7 +318,6 @@ object NumericAttribute extends AttributeFactory {
  *                  defined.
  * @param values optional values. At most one of `numValues` and `values` can be defined.
  */
-@DeveloperApi
 class NominalAttribute private[ml] (
     override val name: Option[String] = None,
     override val index: Option[Int] = None,
@@ -464,10 +451,8 @@ class NominalAttribute private[ml] (
 }
 
 /**
- * :: DeveloperApi ::
  * Factory methods for nominal attributes.
  */
-@DeveloperApi
 object NominalAttribute extends AttributeFactory {
 
   /** The default nominal attribute. */
@@ -487,13 +472,11 @@ object NominalAttribute extends AttributeFactory {
 }
 
 /**
- * :: DeveloperApi ::
  * A binary attribute.
  * @param name optional name
  * @param index optional index
  * @param values optional values. If set, its size must be 2.
  */
-@DeveloperApi
 class BinaryAttribute private[ml] (
     override val name: Option[String] = None,
     override val index: Option[Int] = None,
@@ -566,10 +549,8 @@ class BinaryAttribute private[ml] (
 }
 
 /**
- * :: DeveloperApi ::
  * Factory methods for binary attributes.
  */
-@DeveloperApi
 object BinaryAttribute extends AttributeFactory {
 
   /** The default binary attribute. */
@@ -586,10 +567,8 @@ object BinaryAttribute extends AttributeFactory {
 }
 
 /**
- * :: DeveloperApi ::
  * An unresolved attribute.
  */
-@DeveloperApi
 object UnresolvedAttribute extends Attribute {
 
   override def attrType: AttributeType = AttributeType.Unresolved

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

@@ -18,7 +18,7 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.SparkException
-import org.apache.spark.annotation.{DeveloperApi, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.ml.{PredictionModel, Predictor, PredictorParams}
 import org.apache.spark.ml.feature.{Instance, LabeledPoint}
 import org.apache.spark.ml.linalg.{Vector, VectorUDT}
@@ -62,16 +62,13 @@ private[spark] trait ClassifierParams
 }
 
 /**
- * :: DeveloperApi ::
- *
  * Single-label binary or multiclass classification.
  * Classes are indexed {0, 1, ..., numClasses - 1}.
  *
  * @tparam FeaturesType  Type of input features.  E.g., `Vector`
  * @tparam E  Concrete Estimator type
  * @tparam M  Concrete Model type
  */
-@DeveloperApi
 abstract class Classifier[
     FeaturesType,
     E <: Classifier[FeaturesType, E, M],
@@ -166,15 +163,12 @@ abstract class Classifier[
 }
 
 /**
- * :: DeveloperApi ::
- *
  * Model produced by a [[Classifier]].
  * Classes are indexed {0, 1, ..., numClasses - 1}.
  *
  * @tparam FeaturesType  Type of input features.  E.g., `Vector`
  * @tparam M  Concrete Model type
  */
-@DeveloperApi
 abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[FeaturesType, M]]
   extends PredictionModel[FeaturesType, M] with ClassifierParams {
 

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

@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.classification
 
-import org.apache.spark.annotation.{DeveloperApi, Since}
+import org.apache.spark.annotation.Since
 import org.apache.spark.ml.linalg.{DenseVector, Vector, VectorUDT}
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.SchemaUtils
@@ -41,15 +41,12 @@ private[ml] trait ProbabilisticClassifierParams
 
 
 /**
- * :: DeveloperApi ::
- *
  * Single-label binary or multiclass classifier which can output class conditional probabilities.
  *
  * @tparam FeaturesType  Type of input features.  E.g., `Vector`
  * @tparam E  Concrete Estimator type
  * @tparam M  Concrete Model type
  */
-@DeveloperApi
 abstract class ProbabilisticClassifier[
     FeaturesType,
     E <: ProbabilisticClassifier[FeaturesType, E, M],
@@ -65,15 +62,12 @@ abstract class ProbabilisticClassifier[
 
 
 /**
- * :: DeveloperApi ::
- *
  * Model produced by a [[ProbabilisticClassifier]].
  * Classes are indexed {0, 1, ..., numClasses - 1}.
  *
  * @tparam FeaturesType  Type of input features.  E.g., `Vector`
  * @tparam M  Concrete Model type
  */
-@DeveloperApi
 abstract class ProbabilisticClassificationModel[
     FeaturesType,
     M <: ProbabilisticClassificationModel[FeaturesType, M]]