Also try to fix a few doc issues while we're here, that still cause warnings

Author: srowen

core/src/main/scala/org/apache/spark/rpc/RpcCallContext.scala

@@ -24,7 +24,7 @@ package org.apache.spark.rpc
 private[spark] trait RpcCallContext {
 
   /**
-   * Reply a message to the sender. If the sender is [[RpcEndpoint]], its [[RpcEndpoint.receive]]
+   * Reply a message to the sender. If the sender is [[RpcEndpoint]], its `RpcEndpoint.receive`
    * will be called.
    */
   def reply(response: Any): Unit

core/src/main/scala/org/apache/spark/status/api/v1/ApiRootResource.scala

@@ -85,8 +85,8 @@ private[spark] trait UIRoot {
   def getApplicationInfo(appId: String): Option[ApplicationInfo]
 
   /**
-   * Write the event logs for the given app to the [[ZipOutputStream]] instance. If attemptId is
-   * [[None]], event logs for all attempts of this application will be written out.
+   * Write the event logs for the given app to the `ZipOutputStream` instance. If attemptId is
+   * `None`, event logs for all attempts of this application will be written out.
    */
   def writeEventLogs(appId: String, attemptId: Option[String], zipStream: ZipOutputStream): Unit = {
     Response.serverError()

core/src/main/scala/org/apache/spark/util/SizeEstimator.scala

@@ -34,7 +34,7 @@ import org.apache.spark.util.collection.OpenHashSet
 /**
  * A trait that allows a class to give [[SizeEstimator]] more accurate size estimation.
  * When a class extends it, [[SizeEstimator]] will query the `estimatedSize` first.
- * If `estimatedSize` does not return [[None]], [[SizeEstimator]] will use the returned size
+ * If `estimatedSize` does not return `None`, [[SizeEstimator]] will use the returned size
  * as the size of the object. Otherwise, [[SizeEstimator]] will do the estimation work.
  * The difference between a [[KnownSizeEstimation]] and
  * [[org.apache.spark.util.collection.SizeTracker]] is that, a

mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala

@@ -371,15 +371,15 @@ private[ann] trait TopologyModel extends Serializable {
   def forward(data: BDM[Double], includeLastLayer: Boolean): Array[BDM[Double]]
 
   /**
-   * Prediction of the model. See {@link ProbabilisticClassificationModel}
+   * Prediction of the model. See `ProbabilisticClassificationModel``
    *
    * @param features input features
    * @return prediction
    */
   def predict(features: Vector): Vector
 
   /**
-   * Raw prediction of the model. See {@link ProbabilisticClassificationModel}
+   * Raw prediction of the model. See `ProbabilisticClassificationModel`
    *
    * @param features input features
    * @return raw prediction
@@ -389,7 +389,7 @@ private[ann] trait TopologyModel extends Serializable {
   def predictRaw(features: Vector): Vector
 
   /**
-   * Probability of the model. See {@link ProbabilisticClassificationModel}
+   * Probability of the model. See `ProbabilisticClassificationModel`
    *
    * @param rawPrediction raw prediction vector
    * @return probability

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

@@ -121,7 +121,7 @@ sealed abstract class Attribute extends Serializable {
 private[attribute] trait AttributeFactory {
 
   /**
-   * Creates an [[Attribute]] from a [[Metadata]] instance.
+   * Creates an [[Attribute]] from a `Metadata` instance.
    */
   private[attribute] def fromMetadata(metadata: Metadata): Attribute
 

mllib/src/main/scala/org/apache/spark/ml/stat/Correlation.scala

@@ -49,7 +49,7 @@ object Correlation {
    *               Supported: `pearson` (default), `spearman`
    * @return A dataframe that contains the correlation matrix of the column of vectors. This
    *         dataframe contains a single row and a single column of name
-   *         '$METHODNAME($COLUMN)'.
+   *         `$METHODNAME($COLUMN)`.
    * @throws IllegalArgumentException if the column is not a valid column in the dataset, or if
    *                                  the content of this column is not of type Vector.
    *

mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala

@@ -40,39 +40,39 @@ private[ml] trait DecisionTreeParams extends PredictorParams
   with HasCheckpointInterval with HasSeed with HasWeightCol {
 
   /**
-   * Maximum depth of the tree (>= 0).
+   * Maximum depth of the tree (nonnegative).
    * E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
    * (default = 5)
    * @group param
    */
   final val maxDepth: IntParam =
-    new IntParam(this, "maxDepth", "Maximum depth of the tree. (>= 0)" +
+    new IntParam(this, "maxDepth", "Maximum depth of the tree. (Nonnegative)" +
       " E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.",
       ParamValidators.gtEq(0))
 
   /**
    * Maximum number of bins used for discretizing continuous features and for choosing how to split
    * on features at each node.  More bins give higher granularity.
-   * Must be >= 2 and >= number of categories in any categorical feature.
+   * Must be at least 2 and at least number of categories in any categorical feature.
    * (default = 32)
    * @group param
    */
   final val maxBins: IntParam = new IntParam(this, "maxBins", "Max number of bins for" +
-    " discretizing continuous features.  Must be >=2 and >= number of categories for any" +
-    " categorical feature.", ParamValidators.gtEq(2))
+    " discretizing continuous features.  Must be at least 2 and at least number of categories" +
+    " for any categorical feature.", ParamValidators.gtEq(2))
 
   /**
    * Minimum number of instances each child must have after split.
    * If a split causes the left or right child to have fewer than minInstancesPerNode,
    * the split will be discarded as invalid.
-   * Should be >= 1.
+   * Must be at least 1.
    * (default = 1)
    * @group param
    */
   final val minInstancesPerNode: IntParam = new IntParam(this, "minInstancesPerNode", "Minimum" +
     " number of instances each child must have after split.  If a split causes the left or right" +
     " child to have fewer than minInstancesPerNode, the split will be discarded as invalid." +
-    " Should be >= 1.", ParamValidators.gtEq(1))
+    " Must be at least 1.", ParamValidators.gtEq(1))
 
   /**
    * Minimum fraction of the weighted sample count that each child must have after split.
@@ -91,7 +91,7 @@ private[ml] trait DecisionTreeParams extends PredictorParams
 
   /**
    * Minimum information gain for a split to be considered at a tree node.
-   * Should be >= 0.0.
+   * Should be at least 0.0.
    * (default = 0.0)
    * @group param
    */
@@ -316,7 +316,7 @@ private[ml] trait TreeEnsembleParams extends DecisionTreeParams {
    * Supported options:
    *  - "auto": Choose automatically for task:
    *            If numTrees == 1, set to "all."
-   *            If numTrees > 1 (forest), set to "sqrt" for classification and
+   *            If numTrees greater than 1 (forest), set to "sqrt" for classification and
    *              to "onethird" for regression.
    *  - "all": use all features
    *  - "onethird": use 1/3 of the features
@@ -361,8 +361,8 @@ private[ml] trait TreeEnsembleParams extends DecisionTreeParams {
 private[ml] trait RandomForestParams extends TreeEnsembleParams {
 
   /**
-   * Number of trees to train (>= 1).
-   * If 1, then no bootstrapping is used.  If > 1, then bootstrapping is done.
+   * Number of trees to train (at least 1).
+   * If 1, then no bootstrapping is used.  If greater than 1, then bootstrapping is done.
    * TODO: Change to always do bootstrapping (simpler).  SPARK-7130
    * (default = 20)
    *
@@ -371,7 +371,8 @@ private[ml] trait RandomForestParams extends TreeEnsembleParams {
    * are a bit different.
    * @group param
    */
-  final val numTrees: IntParam = new IntParam(this, "numTrees", "Number of trees to train (>= 1)",
+  final val numTrees: IntParam =
+    new IntParam(this, "numTrees", "Number of trees to train (at least 1)",
     ParamValidators.gtEq(1))
 
   setDefault(numTrees -> 20)

mllib/src/main/scala/org/apache/spark/mllib/stat/test/StreamingTestMethod.scala

@@ -33,7 +33,7 @@ import org.apache.spark.util.StatCounter
 /**
  * Significance testing methods for [[StreamingTest]]. New 2-sample statistical significance tests
  * should extend [[StreamingTestMethod]] and introduce a new entry in
- * [[StreamingTestMethod.TEST_NAME_TO_OBJECT]]
+ * `StreamingTestMethod.TEST_NAME_TO_OBJECT`
  */
 private[stat] sealed trait StreamingTestMethod extends Serializable {
 

sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClient.scala

@@ -41,7 +41,7 @@ private[hive] trait HiveClient {
 
   /**
    * Return the associated Hive SessionState of this [[HiveClientImpl]]
-   * @return [[Any]] not SessionState to avoid linkage error
+   * @return `Any` not SessionState to avoid linkage error
    */
   def getState: Any
 
@@ -76,7 +76,7 @@ private[hive] trait HiveClient {
   /** Return whether a table/view with the specified name exists. */
   def tableExists(dbName: String, tableName: String): Boolean
 
-  /** Returns the specified table, or throws [[NoSuchTableException]]. */
+  /** Returns the specified table, or throws `NoSuchTableException`. */
   final def getTable(dbName: String, tableName: String): CatalogTable = {
     getTableOption(dbName, tableName).getOrElse(throw new NoSuchTableException(dbName, tableName))
   }
@@ -166,7 +166,7 @@ private[hive] trait HiveClient {
       table: String,
       newParts: Seq[CatalogTablePartition]): Unit
 
-  /** Returns the specified partition, or throws [[NoSuchPartitionException]]. */
+  /** Returns the specified partition, or throws `NoSuchPartitionException`. */
   final def getPartition(
       dbName: String,
       tableName: String,