[SPARK-3249][DOC] Fix links in ScalaDoc that cause warning messages in sbt/sbt unidoc

core/src/main/scala/org/apache/spark/Accumulator.scala

@@ -24,8 +24,7 @@ package org.apache.spark
  * They can be used to implement counters (as in MapReduce) or sums. Spark natively supports
  * accumulators of numeric value types, and programmers can add support for new types.
  *
- * An accumulator is created from an initial value `v` by calling
- * [[SparkContext#accumulator SparkContext.accumulator]].
+ * An accumulator is created from an initial value `v` by calling `SparkContext.accumulator`.
  * Tasks running on the cluster can then add to it using the `+=` operator.
  * However, they cannot read its value. Only the driver program can read the accumulator's value,
  * using its [[#value]] method.

core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala

@@ -166,7 +166,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
    * Return a subset of this RDD sampled by key (via stratified sampling) containing exactly
    * math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
    *
-   * This method differs from [[sampleByKey]] in that we make additional passes over the RDD to
+   * This method differs from `sampleByKey` in that we make additional passes over the RDD to
    * create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate)
    * over all key values with a 99.99% confidence. When sampling without replacement, we need one
    * additional pass over the RDD to guarantee sample size; when sampling with replacement, we need
@@ -184,7 +184,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
    * Return a subset of this RDD sampled by key (via stratified sampling) containing exactly
    * math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
    *
-   * This method differs from [[sampleByKey]] in that we make additional passes over the RDD to
+   * This method differs from `sampleByKey` in that we make additional passes over the RDD to
    * create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate)
    * over all key values with a 99.99% confidence. When sampling without replacement, we need one
    * additional pass over the RDD to guarantee sample size; when sampling with replacement, we need

core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala

@@ -393,7 +393,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   def treeReduce(f: JFunction2[T, T, T], depth: Int): T = rdd.treeReduce(f, depth)
 
   /**
-   * [[org.apache.spark.api.java.JavaRDDLike#treeReduce]] with suggested depth 2.
+   * `org.apache.spark.api.java.JavaRDDLike.treeReduce` with suggested depth 2.
    */
   def treeReduce(f: JFunction2[T, T, T]): T = treeReduce(f, 2)
 
@@ -440,7 +440,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   }
 
   /**
-   * [[org.apache.spark.api.java.JavaRDDLike#treeAggregate]] with suggested depth 2.
+   * `org.apache.spark.api.java.JavaRDDLike.treeAggregate` with suggested depth 2.
    */
   def treeAggregate[U](
       zeroValue: U,

core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala

@@ -109,7 +109,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * functions. This method is here for backward compatibility. It does not provide combiner
    * classtag information to the shuffle.
    *
-   * @see [[combineByKeyWithClassTag]]
+   * @see `combineByKeyWithClassTag`
    */
   def combineByKey[C](
       createCombiner: V => C,
@@ -127,7 +127,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * This method is here for backward compatibility. It does not provide combiner
    * classtag information to the shuffle.
    *
-   * @see [[combineByKeyWithClassTag]]
+   * @see `combineByKeyWithClassTag`
    */
   def combineByKey[C](
       createCombiner: V => C,
@@ -608,7 +608,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * existing partitioner/parallelism level. This method is here for backward compatibility. It
    * does not provide combiner classtag information to the shuffle.
    *
-   * @see [[combineByKeyWithClassTag]]
+   * @see `combineByKeyWithClassTag`
    */
   def combineByKey[C](
       createCombiner: V => C,

core/src/main/scala/org/apache/spark/rdd/RDD.scala

@@ -1841,7 +1841,7 @@ abstract class RDD[T: ClassTag](
  * Defines implicit functions that provide extra functionalities on RDDs of specific types.
  *
  * For example, [[RDD.rddToPairRDDFunctions]] converts an RDD into a [[PairRDDFunctions]] for
- * key-value-pair RDDs, and enabling extra functionalities such as [[PairRDDFunctions.reduceByKey]].
+ * key-value-pair RDDs, and enabling extra functionalities such as `PairRDDFunctions.reduceByKey`.
  */
 object RDD {
 

graphx/src/main/scala/org/apache/spark/graphx/Graph.scala

@@ -331,7 +331,7 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab
 
   /**
    * Merges multiple edges between two vertices into a single edge. For correct results, the graph
-   * must have been partitioned using [[partitionBy]].
+   * must have been partitioned using `partitionBy`.
    *
    * @param merge the user-supplied commutative associative function to merge edge attributes
    *              for duplicate edges.

graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala

@@ -428,7 +428,7 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) extends Seriali
    * Compute the connected component membership of each vertex and return a graph with the vertex
    * value containing the lowest vertex id in the connected component containing that vertex.
    *
-   * @see [[org.apache.spark.graphx.lib.ConnectedComponents$#run]]
+   * @see `org.apache.spark.graphx.lib.ConnectedComponents.run`
    */
   def connectedComponents(): Graph[VertexId, ED] = {
     ConnectedComponents.run(graph)
@@ -438,7 +438,7 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) extends Seriali
    * Compute the connected component membership of each vertex and return a graph with the vertex
    * value containing the lowest vertex id in the connected component containing that vertex.
    *
-   * @see [[org.apache.spark.graphx.lib.ConnectedComponents$#run]]
+   * @see `org.apache.spark.graphx.lib.ConnectedComponents.run`
    */
   def connectedComponents(maxIterations: Int): Graph[VertexId, ED] = {
     ConnectedComponents.run(graph, maxIterations)

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

@@ -83,11 +83,11 @@ abstract class PipelineStage extends Params with Logging {
 
 /**
  * A simple pipeline, which acts as an estimator. A Pipeline consists of a sequence of stages, each
- * of which is either an [[Estimator]] or a [[Transformer]]. When [[Pipeline#fit]] is called, the
- * stages are executed in order. If a stage is an [[Estimator]], its [[Estimator#fit]] method will
+ * of which is either an [[Estimator]] or a [[Transformer]]. When `Pipeline.fit` is called, the
+ * stages are executed in order. If a stage is an [[Estimator]], its `Estimator.fit` method will
  * be called on the input dataset to fit a model. Then the model, which is a transformer, will be
  * used to transform the dataset as the input to the next stage. If a stage is a [[Transformer]],
- * its [[Transformer#transform]] method will be called to produce the dataset for the next stage.
+ * its `Transformer.transform` method will be called to produce the dataset for the next stage.
  * The fitted model from a [[Pipeline]] is a [[PipelineModel]], which consists of fitted models and
  * transformers, corresponding to the pipeline stages. If there are no stages, the pipeline acts as
  * an identity transformer.
@@ -121,9 +121,9 @@ class Pipeline @Since("1.4.0") (
 
   /**
    * Fits the pipeline to the input dataset with additional parameters. If a stage is an
-   * [[Estimator]], its [[Estimator#fit]] method will be called on the input dataset to fit a model.
+   * [[Estimator]], its `Estimator.fit` method will be called on the input dataset to fit a model.
    * Then the model, which is a transformer, will be used to transform the dataset as the input to
-   * the next stage. If a stage is a [[Transformer]], its [[Transformer#transform]] method will be
+   * the next stage. If a stage is a [[Transformer]], its `Transformer.transform` method will be
    * called to produce the dataset for the next stage. The fitted model from a [[Pipeline]] is an
    * [[PipelineModel]], which consists of fitted models and transformers, corresponding to the
    * pipeline stages. If there are no stages, the output model acts as an identity transformer.

mllib/src/main/scala/org/apache/spark/ml/param/params.scala

@@ -728,7 +728,7 @@ trait Params extends Identifiable with Serializable {
   }
 
   /**
-   * [[extractParamMap]] with no extra values.
+   * `extractParamMap` with no extra values.
    */
   final def extractParamMap(): ParamMap = {
     extractParamMap(ParamMap.empty)

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

@@ -237,7 +237,7 @@ class LocalLDAModel private[spark] (
     vocabSize)
 
   /**
-   * Java-friendly version of [[logLikelihood]]
+   * Java-friendly version of `logLikelihood`
    */
   @Since("1.5.0")
   def logLikelihood(documents: JavaPairRDD[java.lang.Long, Vector]): Double = {
@@ -259,7 +259,9 @@ class LocalLDAModel private[spark] (
     -logLikelihood(documents) / corpusTokenCount
   }
 
-  /** Java-friendly version of [[logPerplexity]] */
+  /**
+   * Java-friendly version of `logPerplexity`
+   */
   @Since("1.5.0")
   def logPerplexity(documents: JavaPairRDD[java.lang.Long, Vector]): Double = {
     logPerplexity(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
@@ -365,7 +367,9 @@ class LocalLDAModel private[spark] (
     }
   }
 
-  /** Get a method usable as a UDF for [[topicDistributions()]] */
+  /**
+   * Get a method usable as a UDF for `topicDistributions()`
+   */
   private[spark] def getTopicDistributionMethod(sc: SparkContext): Vector => Vector = {
     val expElogbeta = exp(LDAUtils.dirichletExpectation(topicsMatrix.asBreeze.toDenseMatrix.t).t)
     val expElogbetaBc = sc.broadcast(expElogbeta)
@@ -414,7 +418,7 @@ class LocalLDAModel private[spark] (
   }
 
   /**
-   * Java-friendly version of [[topicDistributions]]
+   * Java-friendly version of `topicDistributions`
    */
   @Since("1.4.1")
   def topicDistributions(

mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala

@@ -80,7 +80,9 @@ class AssociationRules private[fpm] (
     }.filter(_.confidence >= minConfidence)
   }
 
-  /** Java-friendly version of [[run]]. */
+  /**
+   * Java-friendly version of `run`.
+   */
   @Since("1.5.0")
   def run[Item](freqItemsets: JavaRDD[FreqItemset[Item]]): JavaRDD[Rule[Item]] = {
     val tag = fakeClassTag[Item]

mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala

@@ -218,7 +218,9 @@ class FPGrowth private (
     new FPGrowthModel(freqItemsets)
   }
 
-  /** Java-friendly version of [[run]]. */
+  /**
+   * Java-friendly version of `run`.
+   */
   @Since("1.3.0")
   def run[Item, Basket <: JavaIterable[Item]](data: JavaRDD[Basket]): FPGrowthModel[Item] = {
     implicit val tag = fakeClassTag[Item]

mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala

@@ -279,7 +279,7 @@ object GradientDescent extends Logging {
   }
 
   /**
-   * Alias of [[runMiniBatchSGD]] with convergenceTol set to default value of 0.001.
+   * Alias of `runMiniBatchSGD` with convergenceTol set to default value of 0.001.
    */
   def runMiniBatchSGD(
       data: RDD[(Double, Vector)],