[SPARK-13986][CORE][MLLIB] Remove DeveloperApi-annotations for non-publics

## What changes were proposed in this pull request?

Spark uses `DeveloperApi` annotation, but sometimes it seems to conflict with visibility. This PR tries to fix those conflict by removing annotations for non-publics. The following is the example.

**JobResult.scala**
```scala
DeveloperApi
sealed trait JobResult

DeveloperApi
case object JobSucceeded extends JobResult

-DeveloperApi
private[spark] case class JobFailed(exception: Exception) extends JobResult
```

## How was this patch tested?

Pass the existing Jenkins test.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #11797 from dongjoon-hyun/SPARK-13986.

core/src/main/scala/org/apache/spark/scheduler/JobResult.scala

@@ -29,5 +29,4 @@ sealed trait JobResult
 @DeveloperApi
 case object JobSucceeded extends JobResult
 
-@DeveloperApi
 private[spark] case class JobFailed(exception: Exception) extends JobResult

core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala

@@ -19,17 +19,13 @@ package org.apache.spark.util.collection
 
 import scala.reflect.ClassTag
 
-import org.apache.spark.annotation.DeveloperApi
-
 /**
- * :: DeveloperApi ::
  * A fast hash map implementation for nullable keys. This hash map supports insertions and updates,
  * but not deletions. This map is about 5X faster than java.util.HashMap, while using much less
  * space overhead.
  *
  * Under the hood, it uses our OpenHashSet implementation.
  */
-@DeveloperApi
 private[spark]
 class OpenHashMap[K : ClassTag, @specialized(Long, Int, Double) V: ClassTag](
     initialCapacity: Int)

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

@@ -22,15 +22,12 @@ import org.apache.spark.ml.{PredictionModel, Predictor, PredictorParams}
 
 
 /**
- * :: DeveloperApi ::
- *
  * Single-label regression
  *
  * @tparam FeaturesType  Type of input features.  E.g., [[org.apache.spark.mllib.linalg.Vector]]
  * @tparam Learner  Concrete Estimator type
  * @tparam M  Concrete Model type
  */
-@DeveloperApi
 private[spark] abstract class Regressor[
     FeaturesType,
     Learner <: Regressor[FeaturesType, Learner, M],

mllib/src/main/scala/org/apache/spark/ml/tree/impl/NodeIdCache.scala

@@ -171,7 +171,6 @@ private[spark] class NodeIdCache(
   }
 }
 
-@DeveloperApi
 private[spark] object NodeIdCache {
   /**
    * Initialize the node Id cache with initial node Id values.

mllib/src/main/scala/org/apache/spark/ml/tuning/ValidatorParams.scala

@@ -17,17 +17,14 @@
 
 package org.apache.spark.ml.tuning
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ml.Estimator
 import org.apache.spark.ml.evaluation.Evaluator
 import org.apache.spark.ml.param.{Param, ParamMap, Params}
 import org.apache.spark.sql.types.StructType
 
 /**
- * :: DeveloperApi ::
  * Common params for [[TrainValidationSplitParams]] and [[CrossValidatorParams]].
  */
-@DeveloperApi
 private[ml] trait ValidatorParams extends Params {
 
   /**

mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala

@@ -156,7 +156,6 @@ sealed trait Matrix extends Serializable {
   def numActives: Int
 }
 
-@DeveloperApi
 private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
 
   override def sqlType: StructType = {

mllib/src/main/scala/org/apache/spark/mllib/tree/impl/NodeIdCache.scala

@@ -173,7 +173,6 @@ private[spark] class NodeIdCache(
   }
 }
 
-@DeveloperApi
 private[spark] object NodeIdCache {
   /**
    * Initialize the node Id cache with initial node Id values.

mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala

@@ -79,7 +79,6 @@ private[spark] object InformationGainStats {
 }
 
 /**
- * :: DeveloperApi ::
  * Impurity statistics for each split
  * @param gain information gain value
  * @param impurity current node impurity
@@ -89,7 +88,6 @@ private[spark] object InformationGainStats {
  * @param valid whether the current split satisfies minimum info gain or
  *              minimum number of instances per node
  */
-@DeveloperApi
 private[spark] class ImpurityStats(
     val gain: Double,
     val impurity: Double,