diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala
index 308a1ed5fa963e71ad9aefe7a24b8e4bd0cb873d..fe912e639bcbc349f976b7acf01f17e9d46782d8 100644
--- a/core/src/main/scala/org/apache/spark/SparkConf.scala
+++ b/core/src/main/scala/org/apache/spark/SparkConf.scala
@@ -262,7 +262,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging with Seria
/**
* Get a time parameter as seconds; throws a NoSuchElementException if it's not set. If no
* suffix is provided then seconds are assumed.
- * @throws java.util.NoSuchElementException
+ * @throws java.util.NoSuchElementException If the time parameter is not set
*/
def getTimeAsSeconds(key: String): Long = {
Utils.timeStringAsSeconds(get(key))
@@ -279,7 +279,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging with Seria
/**
* Get a time parameter as milliseconds; throws a NoSuchElementException if it's not set. If no
* suffix is provided then milliseconds are assumed.
- * @throws java.util.NoSuchElementException
+ * @throws java.util.NoSuchElementException If the time parameter is not set
*/
def getTimeAsMs(key: String): Long = {
Utils.timeStringAsMs(get(key))
@@ -296,7 +296,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging with Seria
/**
* Get a size parameter as bytes; throws a NoSuchElementException if it's not set. If no
* suffix is provided then bytes are assumed.
- * @throws java.util.NoSuchElementException
+ * @throws java.util.NoSuchElementException If the size parameter is not set
*/
def getSizeAsBytes(key: String): Long = {
Utils.byteStringAsBytes(get(key))
@@ -320,7 +320,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging with Seria
/**
* Get a size parameter as Kibibytes; throws a NoSuchElementException if it's not set. If no
* suffix is provided then Kibibytes are assumed.
- * @throws java.util.NoSuchElementException
+ * @throws java.util.NoSuchElementException If the size parameter is not set
*/
def getSizeAsKb(key: String): Long = {
Utils.byteStringAsKb(get(key))
@@ -337,7 +337,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging with Seria
/**
* Get a size parameter as Mebibytes; throws a NoSuchElementException if it's not set. If no
* suffix is provided then Mebibytes are assumed.
- * @throws java.util.NoSuchElementException
+ * @throws java.util.NoSuchElementException If the size parameter is not set
*/
def getSizeAsMb(key: String): Long = {
Utils.byteStringAsMb(get(key))
@@ -354,7 +354,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging with Seria
/**
* Get a size parameter as Gibibytes; throws a NoSuchElementException if it's not set. If no
* suffix is provided then Gibibytes are assumed.
- * @throws java.util.NoSuchElementException
+ * @throws java.util.NoSuchElementException If the size parameter is not set
*/
def getSizeAsGb(key: String): Long = {
Utils.byteStringAsGb(get(key))
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index d7bfdbad8442f1975b3e6663828ed19df2ff116b..41093bdb858c0e98b6b3f8d1124d50e76ba782e0 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -496,7 +496,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
* or `PairRDDFunctions.reduceByKey` will provide much better performance.
*
* @note As currently implemented, groupByKey must be able to hold all the key-value pairs for any
- * key in memory. If a key has too many values, it can result in an [[OutOfMemoryError]].
+ * key in memory. If a key has too many values, it can result in an `OutOfMemoryError`.
*/
def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = self.withScope {
// groupByKey shouldn't use map side combine because map side combine does not
@@ -520,7 +520,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
* or `PairRDDFunctions.reduceByKey` will provide much better performance.
*
* @note As currently implemented, groupByKey must be able to hold all the key-value pairs for any
- * key in memory. If a key has too many values, it can result in an [[OutOfMemoryError]].
+ * key in memory. If a key has too many values, it can result in an `OutOfMemoryError`.
*/
def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])] = self.withScope {
groupByKey(new HashPartitioner(numPartitions))
diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
index 1b12af75864eaf9a9369d80ad46c25f1d1acf9b4..4331addb44172ba5e2b421e5fabca28084c25062 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
@@ -294,7 +294,7 @@ private[spark] trait SparkListenerInterface {
/**
* :: DeveloperApi ::
- * A default implementation for [[SparkListenerInterface]] that has no-op implementations for
+ * A default implementation for `SparkListenerInterface` that has no-op implementations for
* all callbacks.
*
* Note that this is an internal interface which might change in different Spark releases.
diff --git a/core/src/main/scala/org/apache/spark/scheduler/package.scala b/core/src/main/scala/org/apache/spark/scheduler/package.scala
index f0dbfc2ac5f48b96975368246c57f814f63b555e..4847c41710b2bc7baca2093280ffbe72063282f8 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/package.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/package.scala
@@ -18,7 +18,7 @@
package org.apache.spark
/**
- * Spark's scheduling components. This includes the [[org.apache.spark.scheduler.DAGScheduler]] and
- * lower level [[org.apache.spark.scheduler.TaskScheduler]].
+ * Spark's scheduling components. This includes the `org.apache.spark.scheduler.DAGScheduler` and
+ * lower level `org.apache.spark.scheduler.TaskScheduler`.
*/
package object scheduler
diff --git a/core/src/main/scala/org/apache/spark/storage/TopologyMapper.scala b/core/src/main/scala/org/apache/spark/storage/TopologyMapper.scala
index a0f0fdef8e9484b2f9b828f7665f1e91e1b8929b..a150a8e3636e41bfafc053e701fd85b74227a74d 100644
--- a/core/src/main/scala/org/apache/spark/storage/TopologyMapper.scala
+++ b/core/src/main/scala/org/apache/spark/storage/TopologyMapper.scala
@@ -60,7 +60,7 @@ class DefaultTopologyMapper(conf: SparkConf) extends TopologyMapper(conf) with L
/**
* A simple file based topology mapper. This expects topology information provided as a
- * [[java.util.Properties]] file. The name of the file is obtained from SparkConf property
+ * `java.util.Properties` file. The name of the file is obtained from SparkConf property
* `spark.storage.replication.topologyFile`. To use this topology mapper, set the
* `spark.storage.replication.topologyMapper` property to
* [[org.apache.spark.storage.FileBasedTopologyMapper]]
diff --git a/docs/js/api-docs.js b/docs/js/api-docs.js
index 96c63cc12716f5ac2e1640688c8c4d650bfe3665..13514e11b93324facae15eb814678694d3f28540 100644
--- a/docs/js/api-docs.js
+++ b/docs/js/api-docs.js
@@ -50,7 +50,7 @@ $(document).ready(function() {
MathJax.Hub.Config({
displayAlign: "left",
tex2jax: {
- inlineMath: [ ["$", "$"], ["\\\\(","\\\\)"] ],
+ inlineMath: [ ["$", "$"], ["\\(","\\)"] ],
displayMath: [ ["$$","$$"], ["\\[", "\\]"] ],
processEscapes: true,
skipTags: ['script', 'noscript', 'style', 'textarea', 'pre', 'a']
diff --git a/mllib-local/src/main/scala/org/apache/spark/ml/linalg/Vectors.scala b/mllib-local/src/main/scala/org/apache/spark/ml/linalg/Vectors.scala
index 22e4ec693b1f7ac4e30823d6929da7b79a86ee46..8e166ba0ff51a3f4fa896acef2e8aa471b683fd1 100644
--- a/mllib-local/src/main/scala/org/apache/spark/ml/linalg/Vectors.scala
+++ b/mllib-local/src/main/scala/org/apache/spark/ml/linalg/Vectors.scala
@@ -169,7 +169,7 @@ sealed trait Vector extends Serializable {
/**
* Factory methods for [[org.apache.spark.ml.linalg.Vector]].
* We don't use the name `Vector` because Scala imports
- * [[scala.collection.immutable.Vector]] by default.
+ * `scala.collection.immutable.Vector` by default.
*/
@Since("2.0.0")
object Vectors {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
index 215f9d86f1be12c151a469317ea9b87cc497b1fe..08b0cb9b8f6a5f1525ba00170e24c7cb3aa733c4 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
@@ -40,7 +40,7 @@ private[ml] trait PredictorParams extends Params
* @param schema input schema
* @param fitting whether this is in fitting
* @param featuresDataType SQL DataType for FeaturesType.
- * E.g., [[VectorUDT]] for vector features.
+ * E.g., `VectorUDT` for vector features.
* @return output schema
*/
protected def validateAndTransformSchema(
@@ -72,7 +72,7 @@ private[ml] trait PredictorParams extends Params
* in `fit()`.
*
* @tparam FeaturesType Type of features.
- * E.g., [[VectorUDT]] for vector features.
+ * E.g., `VectorUDT` for vector features.
* @tparam Learner Specialization of this class. If you subclass this type, use this type
* parameter to specify the concrete type.
* @tparam M Specialization of [[PredictionModel]]. If you subclass this type, use this type
@@ -122,7 +122,7 @@ abstract class Predictor[
/**
* Train a model using the given dataset and parameters.
- * Developers can implement this instead of [[fit()]] to avoid dealing with schema validation
+ * Developers can implement this instead of `fit()` to avoid dealing with schema validation
* and copying parameters into the model.
*
* @param dataset Training dataset
@@ -133,7 +133,7 @@ abstract class Predictor[
/**
* Returns the SQL DataType corresponding to the FeaturesType type parameter.
*
- * This is used by [[validateAndTransformSchema()]].
+ * This is used by `validateAndTransformSchema()`.
* This workaround is needed since SQL has different APIs for Scala and Java.
*
* The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.
@@ -160,7 +160,7 @@ abstract class Predictor[
* Abstraction for a model for prediction tasks (regression and classification).
*
* @tparam FeaturesType Type of features.
- * E.g., [[VectorUDT]] for vector 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.
*/
@@ -181,7 +181,7 @@ abstract class PredictionModel[FeaturesType, M <: PredictionModel[FeaturesType,
/**
* Returns the SQL DataType corresponding to the FeaturesType type parameter.
*
- * This is used by [[validateAndTransformSchema()]].
+ * This is used by `validateAndTransformSchema()`.
* This workaround is needed since SQL has different APIs for Scala and Java.
*
* The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.
@@ -197,7 +197,7 @@ abstract class PredictionModel[FeaturesType, M <: PredictionModel[FeaturesType,
* the predictions as a new column [[predictionCol]].
*
* @param dataset input dataset
- * @return transformed dataset with [[predictionCol]] of type [[Double]]
+ * @return transformed dataset with [[predictionCol]] of type `Double`
*/
override def transform(dataset: Dataset[_]): DataFrame = {
transformSchema(dataset.schema, logging = true)
@@ -219,7 +219,7 @@ abstract class PredictionModel[FeaturesType, M <: PredictionModel[FeaturesType,
/**
* Predict label for the given features.
- * This internal method is used to implement [[transform()]] and output [[predictionCol]].
+ * This internal method is used to implement `transform()` and output [[predictionCol]].
*/
protected def predict(features: FeaturesType): Double
}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala
index 7fbfee75e96a9866d5f8261b6820d0fac27c257d..1cd2b1ad84092f1bd3da9482ae3c0c44be868f49 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala
@@ -126,7 +126,7 @@ private[attribute] trait AttributeFactory {
private[attribute] def fromMetadata(metadata: Metadata): Attribute
/**
- * Creates an [[Attribute]] from a [[StructField]] instance, optionally preserving name.
+ * Creates an [[Attribute]] from a `StructField` instance, optionally preserving name.
*/
private[ml] def decodeStructField(field: StructField, preserveName: Boolean): Attribute = {
require(field.dataType.isInstanceOf[NumericType])
@@ -145,7 +145,7 @@ private[attribute] trait AttributeFactory {
}
/**
- * Creates an [[Attribute]] from a [[StructField]] instance.
+ * Creates an [[Attribute]] from a `StructField` instance.
*/
def fromStructField(field: StructField): Attribute = decodeStructField(field, false)
}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/package.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/package.scala
index f6964054db8396968bd58b2052c370a19f26d893..25ce0282b12749ffd6c8739e7ea34de2e29708af 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/attribute/package.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/package.scala
@@ -23,7 +23,7 @@ import org.apache.spark.sql.DataFrame
/**
* ==ML attributes==
*
- * The ML pipeline API uses [[DataFrame]]s as ML datasets.
+ * The ML pipeline API uses `DataFrame`s as ML datasets.
* Each dataset consists of typed columns, e.g., string, double, vector, etc.
* However, knowing only the column type may not be sufficient to handle the data properly.
* For instance, a double column with values 0.0, 1.0, 2.0, ... may represent some label indices,
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
index 8fd6b70d4ddb46f0b16e8645b3d0f600588ce229..d8608d885d6f177156f4488dbd981c0c9807fe32 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
@@ -71,7 +71,7 @@ abstract class Classifier[
* and put it in an RDD with strong types.
*
* @param dataset DataFrame with columns for labels ([[org.apache.spark.sql.types.NumericType]])
- * and features ([[Vector]]).
+ * and features (`Vector`).
* @param numClasses Number of classes label can take. Labels must be integers in the range
* [0, numClasses).
* @throws SparkException if any label is not an integer >= 0
@@ -94,7 +94,7 @@ abstract class Classifier[
* by finding the maximum label value.
*
* Label validation (ensuring all labels are integers >= 0) needs to be handled elsewhere,
- * such as in [[extractLabeledPoints()]].
+ * such as in `extractLabeledPoints()`.
*
* @param dataset Dataset which contains a column [[labelCol]]
* @param maxNumClasses Maximum number of classes allowed when inferred from data. If numClasses
@@ -150,7 +150,7 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur
/**
* Transforms dataset by reading from [[featuresCol]], and appending new columns as specified by
* parameters:
- * - predicted labels as [[predictionCol]] of type [[Double]]
+ * - predicted labels as [[predictionCol]] of type `Double`
* - raw predictions (confidences) as [[rawPredictionCol]] of type `Vector`.
*
* @param dataset input dataset
@@ -192,10 +192,10 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur
/**
* Predict label for the given features.
- * This internal method is used to implement [[transform()]] and output [[predictionCol]].
+ * This internal method is used to implement `transform()` and output [[predictionCol]].
*
* This default implementation for classification predicts the index of the maximum value
- * from [[predictRaw()]].
+ * from `predictRaw()`.
*/
override protected def predict(features: FeaturesType): Double = {
raw2prediction(predictRaw(features))
@@ -205,7 +205,7 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur
* Raw prediction for each possible label.
* The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives
* a measure of confidence in each possible label (where larger = more confident).
- * This internal method is used to implement [[transform()]] and output [[rawPredictionCol]].
+ * This internal method is used to implement `transform()` and output [[rawPredictionCol]].
*
* @return vector where element i is the raw prediction for label i.
* This raw prediction may be any real number, where a larger value indicates greater
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 93cc1e6f09727befb59ed50c4a697500cebfc52d..95c1337ed5608179b48c34507f321f3b65e3bd54 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
@@ -225,7 +225,7 @@ class MultilayerPerceptronClassifier @Since("1.5.0") (
/**
* Train a model using the given dataset and parameters.
- * Developers can implement this instead of [[fit()]] to avoid dealing with schema validation
+ * Developers can implement this instead of `fit()` to avoid dealing with schema validation
* and copying parameters into the model.
*
* @param dataset Training dataset
@@ -321,7 +321,7 @@ class MultilayerPerceptronClassificationModel private[ml] (
/**
* Predict label for the given features.
- * This internal method is used to implement [[transform()]] and output [[predictionCol]].
+ * This internal method is used to implement `transform()` and output [[predictionCol]].
*/
override protected def predict(features: Vector): Double = {
LabelConverter.decodeLabel(mlpModel.predict(features))
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
index e89da6ff8bdd72b071611aedfdc7cb1f339dca8b..ef08134809915578daf44545b6190e25d167ed83 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
@@ -93,7 +93,7 @@ abstract class ProbabilisticClassificationModel[
/**
* Transforms dataset by reading from [[featuresCol]], and appending new columns as specified by
* parameters:
- * - predicted labels as [[predictionCol]] of type [[Double]]
+ * - predicted labels as [[predictionCol]] of type `Double`
* - raw predictions (confidences) as [[rawPredictionCol]] of type `Vector`
* - probability of each class as [[probabilityCol]] of type `Vector`.
*
@@ -158,13 +158,15 @@ abstract class ProbabilisticClassificationModel[
* doing the computation in-place.
* These predictions are also called class conditional probabilities.
*
- * This internal method is used to implement [[transform()]] and output [[probabilityCol]].
+ * This internal method is used to implement `transform()` and output [[probabilityCol]].
*
* @return Estimated class conditional probabilities (modified input vector)
*/
protected def raw2probabilityInPlace(rawPrediction: Vector): Vector
- /** Non-in-place version of [[raw2probabilityInPlace()]] */
+ /**
+ * Non-in-place version of `raw2probabilityInPlace()`
+ */
protected def raw2probability(rawPrediction: Vector): Vector = {
val probs = rawPrediction.copy
raw2probabilityInPlace(probs)
@@ -182,7 +184,7 @@ abstract class ProbabilisticClassificationModel[
* Predict the probability of each class given the features.
* These predictions are also called class conditional probabilities.
*
- * This internal method is used to implement [[transform()]] and output [[probabilityCol]].
+ * This internal method is used to implement `transform()` and output [[probabilityCol]].
*
* @return Estimated class conditional probabilities
*/
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
index 19978c97d2cfdcdf1b57217fbb93e2a9a26d5d9c..f648deced54cdcfdf16b277f034b434595c2e745 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
@@ -84,7 +84,7 @@ private[feature] trait MinMaxScalerParams extends Params with HasInputCol with H
* $$
* </blockquote>
*
- * For the case $E_{max} == E_{min}$, $Rescaled(e_i) = 0.5 * (max + min)$.
+ * For the case \(E_{max} == E_{min}\), \(Rescaled(e_i) = 0.5 * (max + min)\).
*
* @note Since zero values will probably be transformed to non-zero values, output of the
* transformer will be DenseVector even for sparse input.
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/package.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/package.scala
index 5dd648aecc95c3b0855a450f57b5300f71c43fa7..d75a6dc9377aea3e660a9bf6403cef4e9a4147c6 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/package.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/package.scala
@@ -25,13 +25,13 @@ import org.apache.spark.sql.DataFrame
*
* The `ml.feature` package provides common feature transformers that help convert raw data or
* features into more suitable forms for model fitting.
- * Most feature transformers are implemented as [[Transformer]]s, which transform one [[DataFrame]]
+ * Most feature transformers are implemented as [[Transformer]]s, which transform one `DataFrame`
* into another, e.g., [[HashingTF]].
* Some feature transformers are implemented as [[Estimator]]s, because the transformation requires
* some aggregated information of the dataset, e.g., document frequencies in [[IDF]].
- * For those feature transformers, calling [[Estimator!.fit]] is required to obtain the model first,
+ * For those feature transformers, calling `Estimator.fit` is required to obtain the model first,
* e.g., [[IDFModel]], in order to apply transformation.
- * The transformation is usually done by appending new columns to the input [[DataFrame]], so all
+ * The transformation is usually done by appending new columns to the input `DataFrame`, so all
* input columns are carried over.
*
* We try to make each transformer minimal, so it becomes flexible to assemble feature
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
index ab0620ca7553ac1b64665c3bb6a19c9388600ec8..12ad8002064636ef3e3040854a7260ba19909b45 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
@@ -446,7 +446,7 @@ class StringArrayParam(parent: Params, name: String, doc: String, isValid: Array
def this(parent: Params, name: String, doc: String) =
this(parent, name, doc, ParamValidators.alwaysTrue)
- /** Creates a param pair with a [[java.util.List]] of values (for Java and Python). */
+ /** Creates a param pair with a `java.util.List` of values (for Java and Python). */
def w(value: java.util.List[String]): ParamPair[Array[String]] = w(value.asScala.toArray)
override def jsonEncode(value: Array[String]): String = {
@@ -471,7 +471,7 @@ class DoubleArrayParam(parent: Params, name: String, doc: String, isValid: Array
def this(parent: Params, name: String, doc: String) =
this(parent, name, doc, ParamValidators.alwaysTrue)
- /** Creates a param pair with a [[java.util.List]] of values (for Java and Python). */
+ /** Creates a param pair with a `java.util.List` of values (for Java and Python). */
def w(value: java.util.List[java.lang.Double]): ParamPair[Array[Double]] =
w(value.asScala.map(_.asInstanceOf[Double]).toArray)
@@ -501,7 +501,7 @@ class IntArrayParam(parent: Params, name: String, doc: String, isValid: Array[In
def this(parent: Params, name: String, doc: String) =
this(parent, name, doc, ParamValidators.alwaysTrue)
- /** Creates a param pair with a [[java.util.List]] of values (for Java and Python). */
+ /** Creates a param pair with a `java.util.List` of values (for Java and Python). */
def w(value: java.util.List[java.lang.Integer]): ParamPair[Array[Int]] =
w(value.asScala.map(_.asInstanceOf[Int]).toArray)
@@ -652,7 +652,9 @@ trait Params extends Identifiable with Serializable {
throw new NoSuchElementException(s"Failed to find a default value for ${param.name}"))
}
- /** An alias for [[getOrDefault()]]. */
+ /**
+ * An alias for `getOrDefault()`.
+ */
protected final def $[T](param: Param[T]): T = getOrDefault(param)
/**
@@ -749,14 +751,14 @@ trait Params extends Identifiable with Serializable {
* Copies param values from this instance to another instance for params shared by them.
*
* This handles default Params and explicitly set Params separately.
- * Default Params are copied from and to [[defaultParamMap]], and explicitly set Params are
- * copied from and to [[paramMap]].
+ * Default Params are copied from and to `defaultParamMap`, and explicitly set Params are
+ * copied from and to `paramMap`.
* Warning: This implicitly assumes that this [[Params]] instance and the target instance
* share the same set of default Params.
*
* @param to the target instance, which should work with the same set of default Params as this
* source instance
- * @param extra extra params to be copied to the target's [[paramMap]]
+ * @param extra extra params to be copied to the target's `paramMap`
* @return the target instance with param values copied
*/
protected def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T = {
@@ -822,7 +824,7 @@ final class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any])
this
}
- /** Put param pairs with a [[java.util.List]] of values for Python. */
+ /** Put param pairs with a `java.util.List` of values for Python. */
private[ml] def put(paramPairs: JList[ParamPair[_]]): this.type = {
put(paramPairs.asScala: _*)
}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala
index be356575ca09a39bf9a6366b84664aef33c1b961..c0a1683d3cb6f5ae2efd7ec7b0e1d0ed75b51b8c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala
@@ -40,7 +40,7 @@ private[spark] abstract class Regressor[
/**
* :: DeveloperApi ::
*
- * Model produced by a [[Regressor]].
+ * Model produced by a `Regressor`.
*
* @tparam FeaturesType Type of input features. E.g., [[org.apache.spark.mllib.linalg.Vector]]
* @tparam M Concrete Model type.
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 c0e3801499818b10c38d3cb1d2daa137a7391ccc..09bddcdb810bb95ba0b5185efb9366309b31994e 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
@@ -37,7 +37,7 @@ import org.apache.spark.sql.{SparkSession, SQLContext}
import org.apache.spark.util.Utils
/**
- * Trait for [[MLWriter]] and [[MLReader]].
+ * Trait for `MLWriter` and `MLReader`.
*/
private[util] sealed trait BaseReadWrite {
private var optionSparkSession: Option[SparkSession] = None
@@ -112,7 +112,7 @@ abstract class MLWriter extends BaseReadWrite with Logging {
}
/**
- * [[save()]] handles overwriting and then calls this method. Subclasses should override this
+ * `save()` handles overwriting and then calls this method. Subclasses should override this
* method to implement the actual saving of the instance.
*/
@Since("1.6.0")
@@ -135,13 +135,13 @@ abstract class MLWriter extends BaseReadWrite with Logging {
}
/**
- * Trait for classes that provide [[MLWriter]].
+ * Trait for classes that provide `MLWriter`.
*/
@Since("1.6.0")
trait MLWritable {
/**
- * Returns an [[MLWriter]] instance for this ML instance.
+ * Returns an `MLWriter` instance for this ML instance.
*/
@Since("1.6.0")
def write: MLWriter
@@ -193,7 +193,7 @@ abstract class MLReader[T] extends BaseReadWrite {
}
/**
- * Trait for objects that provide [[MLReader]].
+ * Trait for objects that provide `MLReader`.
*
* @tparam T ML instance type
*/
@@ -201,7 +201,7 @@ abstract class MLReader[T] extends BaseReadWrite {
trait MLReadable[T] {
/**
- * Returns an [[MLReader]] instance for this class.
+ * Returns an `MLReader` instance for this class.
*/
@Since("1.6.0")
def read: MLReader[T]
@@ -235,7 +235,7 @@ trait DefaultParamsReadable[T] extends MLReadable[T] {
}
/**
- * Default [[MLWriter]] implementation for transformers and estimators that contain basic
+ * Default `MLWriter` implementation for transformers and estimators that contain basic
* (json4s-serializable) params and no data. This will not handle more complex params or types with
* data (e.g., models with coefficients).
*
@@ -309,7 +309,7 @@ private[ml] object DefaultParamsWriter {
}
/**
- * Default [[MLReader]] implementation for transformers and estimators that contain basic
+ * Default `MLReader` implementation for transformers and estimators that contain basic
* (json4s-serializable) params and no data. This will not handle more complex params or types with
* data (e.g., models with coefficients).
*
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
index 4cb9200030293d0d26bb104faec2797a1fcb73b3..6c5f529fb8bfd03bd8f3b99bdf6428f5c4a3452b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
@@ -91,7 +91,7 @@ class LDA private (
* distributions over topics ("theta").
*
* This method assumes the Dirichlet distribution is symmetric and can be described by a single
- * [[Double]] parameter. It should fail if docConcentration is asymmetric.
+ * `Double` parameter. It should fail if docConcentration is asymmetric.
*/
@Since("1.3.0")
def getDocConcentration: Double = {
@@ -113,7 +113,7 @@ class LDA private (
*
* If set to a singleton vector Vector(-1), then docConcentration is set automatically. If set to
* singleton vector Vector(t) where t != -1, then t is replicated to a vector of length k during
- * `LDAOptimizer.initialize()`. Otherwise, the [[docConcentration]] vector must be length k.
+ * `LDAOptimizer.initialize()`. Otherwise, the `docConcentration` vector must be length k.
* (default = Vector(-1) = automatic)
*
* Optimizer-specific parameter settings:
@@ -137,7 +137,7 @@ class LDA private (
}
/**
- * Replicates a [[Double]] docConcentration to create a symmetric prior.
+ * Replicates a `Double` docConcentration to create a symmetric prior.
*/
@Since("1.3.0")
def setDocConcentration(docConcentration: Double): this.type = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
index 85a90fa959f938347de85dca3a115468b232ac79..acb83ac31affdc1368bac96fcbaeb0b2d934f079 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
@@ -54,7 +54,7 @@ class AssociationRules private[fpm] (
}
/**
- * Computes the association rules with confidence above [[minConfidence]].
+ * Computes the association rules with confidence above `minConfidence`.
* @param freqItemsets frequent itemset model obtained from [[FPGrowth]]
* @return a `Set[Rule[Item]]` containing the association rules.
*
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
index 635da00b6990e197e9563709d91749252ef67cab..f6b1143272d165dea66569a16393e45385c80f4b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
@@ -52,7 +52,7 @@ class FPGrowthModel[Item: ClassTag] @Since("1.3.0") (
@Since("1.3.0") val freqItemsets: RDD[FreqItemset[Item]])
extends Saveable with Serializable {
/**
- * Generates association rules for the [[Item]]s in [[freqItemsets]].
+ * Generates association rules for the `Item`s in [[freqItemsets]].
* @param confidence minimal confidence of the rules produced
*/
@Since("1.5.0")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 63ea9d3264b0f35ee02a61c8a7c13050e0e3bf69..723addc7150ddf8f91e479793e1d0abfe8dff502 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -273,7 +273,7 @@ class VectorUDT extends UserDefinedType[Vector] {
/**
* Factory methods for [[org.apache.spark.mllib.linalg.Vector]].
* We don't use the name `Vector` because Scala imports
- * [[scala.collection.immutable.Vector]] by default.
+ * `scala.collection.immutable.Vector` by default.
*/
@Since("1.0.0")
object Vectors {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala
index 0efce3c76f15afa953f19960a0a2f0c644ce24bf..88c73241fb555ee106a3db15b6503b4ae15ef510 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala
@@ -78,7 +78,7 @@ abstract class Gradient extends Serializable {
*
* for K classes multiclass classification problem.
*
- * The model weights $w = (w_1, w_2, ..., w_{K-1})^T$ becomes a matrix which has dimension of
+ * The model weights \(w = (w_1, w_2, ..., w_{K-1})^T\) becomes a matrix which has dimension of
* (K-1) * (N+1) if the intercepts are added. If the intercepts are not added, the dimension
* will be (K-1) * N.
*
@@ -93,9 +93,9 @@ abstract class Gradient extends Serializable {
* $$
* </blockquote>
*
- * where $\alpha(i) = 1$ if $i \ne 0$, and
- * $\alpha(i) = 0$ if $i == 0$,
- * $margins_i = x w_i$.
+ * where $\alpha(i) = 1$ if \(i \ne 0\), and
+ * $\alpha(i) = 0$ if \(i == 0\),
+ * \(margins_i = x w_i\).
*
* For optimization, we have to calculate the first derivative of the loss function, and
* a simple calculation shows that
@@ -110,18 +110,19 @@ abstract class Gradient extends Serializable {
* $$
* </blockquote>
*
- * where $\delta_{i, j} = 1$ if $i == j$,
- * $\delta_{i, j} = 0$ if $i != j$, and
+ * where $\delta_{i, j} = 1$ if \(i == j\),
+ * $\delta_{i, j} = 0$ if \(i != j\), and
* multiplier =
* $\exp(margins_i) / (1 + \sum_k^{K-1} \exp(margins_i)) - (1-\alpha(y)\delta_{y, i+1})$
*
* If any of margins is larger than 709.78, the numerical computation of multiplier and loss
* function will be suffered from arithmetic overflow. This issue occurs when there are outliers
* in data which are far away from hyperplane, and this will cause the failing of training once
- * infinity / infinity is introduced. Note that this is only a concern when max(margins) > 0.
+ * infinity / infinity is introduced. Note that this is only a concern when max(margins)
+ * {@literal >} 0.
*
- * Fortunately, when max(margins) = maxMargin > 0, the loss function and the multiplier can be
- * easily rewritten into the following equivalent numerically stable formula.
+ * Fortunately, when max(margins) = maxMargin {@literal >} 0, the loss function and the multiplier
+ * can be easily rewritten into the following equivalent numerically stable formula.
*
* <blockquote>
* $$
@@ -133,7 +134,7 @@ abstract class Gradient extends Serializable {
* \end{align}
* $$
* </blockquote>
-
+ *
* where sum = $\exp(-maxMargin) + \sum_i^{K-1}\exp(margins_i - maxMargin) - 1$.
*
* Note that each term, $(margins_i - maxMargin)$ in $\exp$ is smaller than zero; as a result,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/StreamingTest.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/StreamingTest.scala
index d680237bf687fa6c54a77889aa562c04102dd7ea..551ea357950bab19665933e45b96b1756561acb5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/StreamingTest.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/StreamingTest.scala
@@ -54,7 +54,7 @@ case class BinarySample @Since("1.6.0") (
* cumulative processing, using all batches seen so far.
*
* Different tests may be used for assessing statistical significance depending on assumptions
- * satisfied by data. For more details, see [[StreamingTestMethod]]. The `testMethod` specifies
+ * satisfied by data. For more details, see `StreamingTestMethod`. The `testMethod` specifies
* which test will be used.
*
* Use a builder pattern to construct a streaming test in an application, for example:
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
index 299950785e420320cf4f573a525f2558a0e1eedf..fc1d4125a56490faa6c3b3e9afb36e1d8a64af62 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
@@ -341,7 +341,7 @@ private[tree] sealed class TreeEnsembleModel(
def predict(features: RDD[Vector]): RDD[Double] = features.map(x => predict(x))
/**
- * Java-friendly version of [[org.apache.spark.mllib.tree.model.TreeEnsembleModel#predict]].
+ * Java-friendly version of `org.apache.spark.mllib.tree.model.TreeEnsembleModel.predict`.
*/
def predict(features: JavaRDD[Vector]): JavaRDD[java.lang.Double] = {
predict(features.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]]
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
index c362104b26ffbcb49f6e7fd3a8b728664b33eb81..180c2d130074e32e81e82d389362edf8ddda9c2e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
@@ -48,7 +48,7 @@ object Row {
def apply(values: Any*): Row = new GenericRow(values.toArray)
/**
- * This method can be used to construct a [[Row]] from a [[Seq]] of values.
+ * This method can be used to construct a [[Row]] from a `Seq` of values.
*/
def fromSeq(values: Seq[Any]): Row = new GenericRow(values.toArray)
@@ -283,7 +283,7 @@ trait Row extends Serializable {
def getSeq[T](i: Int): Seq[T] = getAs[Seq[T]](i)
/**
- * Returns the value at position i of array type as [[java.util.List]].
+ * Returns the value at position i of array type as `java.util.List`.
*
* @throws ClassCastException when data type does not match.
*/
@@ -298,7 +298,7 @@ trait Row extends Serializable {
def getMap[K, V](i: Int): scala.collection.Map[K, V] = getAs[Map[K, V]](i)
/**
- * Returns the value at position i of array type as a [[java.util.Map]].
+ * Returns the value at position i of array type as a `java.util.Map`.
*
* @throws ClassCastException when data type does not match.
*/
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
index da5775bc3753a55552aabc4e018e98e3baf39798..1d54ff5825c2e4586cc56373fb1ceb91ef350817 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
@@ -80,7 +80,7 @@ private[sql] object TypeCollection {
/**
* Types that can be ordered/compared. In the long run we should probably make this a trait
- * that can be mixed into each data type, and perhaps create an [[AbstractDataType]].
+ * that can be mixed into each data type, and perhaps create an `AbstractDataType`.
*/
// TODO: Should we consolidate this with RowOrdering.isOrderable?
val Ordered = TypeCollection(
@@ -106,7 +106,7 @@ private[sql] object TypeCollection {
/**
- * An [[AbstractDataType]] that matches any concrete data types.
+ * An `AbstractDataType` that matches any concrete data types.
*/
protected[sql] object AnyDataType extends AbstractDataType {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
index 98efba199ad472edb6903a8b2b6d0032fc6713ee..38c40482fa4d9d738dc5aa8d9ba0c311f750f72f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
@@ -49,7 +49,7 @@ object ArrayType extends AbstractDataType {
* The data type for collections of multiple values.
* Internally these are represented as columns that contain a ``scala.collection.Seq``.
*
- * Please use [[DataTypes.createArrayType()]] to create a specific instance.
+ * Please use `DataTypes.createArrayType()` to create a specific instance.
*
* An [[ArrayType]] object comprises two fields, `elementType: [[DataType]]` and
* `containsNull: Boolean`. The field of `elementType` is used to specify the type of
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index fe34d597dbf1022fd2bf37c33173ca4150be8d1f..a787d5a9a94380a7d5365930cbf328c7c614b8e0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -310,7 +310,7 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
* Lines text format or newline-delimited JSON</a>) and returns the result as
* a `DataFrame`.
*
- * Unless the schema is specified using [[schema]] function, this function goes through the
+ * Unless the schema is specified using `schema` function, this function goes through the
* input once to determine the input schema.
*
* @param jsonRDD input RDD with one JSON object per record
@@ -322,7 +322,7 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
* Loads an `RDD[String]` storing JSON objects (<a href="http://jsonlines.org/">JSON Lines
* text format or newline-delimited JSON</a>) and returns the result as a `DataFrame`.
*
- * Unless the schema is specified using [[schema]] function, this function goes through the
+ * Unless the schema is specified using `schema` function, this function goes through the
* input once to determine the input schema.
*
* @param jsonRDD input RDD with one JSON object per record
@@ -365,7 +365,7 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
*
* This function will go through the input once to determine the input schema if `inferSchema`
* is enabled. To avoid going through the entire data once, disable `inferSchema` option or
- * specify the schema explicitly using [[schema]].
+ * specify the schema explicitly using `schema`.
*
* You can set the following CSV-specific options to deal with CSV files:
* <ul>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
index 89c3a74f4f0676d905c62cc8866fdf4712e99ff2..72945320614bf5a745060fae0bcb4be99ad2fee9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
@@ -152,7 +152,7 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
* The number of distinct values for each column should be less than 1e4. At most 1e6 non-zero
* pair frequencies will be returned.
* The first column of each row will be the distinct values of `col1` and the column names will
- * be the distinct values of `col2`. The name of the first column will be `$col1_$col2`. Counts
+ * be the distinct values of `col2`. The name of the first column will be `col1_col2`. Counts
* will be returned as `Long`s. Pairs that have no occurrences will have zero as their counts.
* Null elements will be replaced by "null", and back ticks will be dropped from elements if they
* exist.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala b/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
index 391c34f1285ed84e7a9227f3d6e68a2e4591532f..ce6e8be8b0ab1390782841f92b1bebf6025bd563 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
@@ -2520,7 +2520,7 @@ class Dataset[T] private[sql](
def unpersist(): this.type = unpersist(blocking = false)
/**
- * Represents the content of the Dataset as an `RDD` of [[T]].
+ * Represents the content of the Dataset as an `RDD` of `T`.
*
* @group basic
* @since 1.6.0
@@ -2534,14 +2534,14 @@ class Dataset[T] private[sql](
}
/**
- * Returns the content of the Dataset as a `JavaRDD` of [[T]]s.
+ * Returns the content of the Dataset as a `JavaRDD` of `T`s.
* @group basic
* @since 1.6.0
*/
def toJavaRDD: JavaRDD[T] = rdd.toJavaRDD()
/**
- * Returns the content of the Dataset as a `JavaRDD` of [[T]]s.
+ * Returns the content of the Dataset as a `JavaRDD` of `T`s.
* @group basic
* @since 1.6.0
*/
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 1a7fd689a04d211b3c63aa334bfd79ec9cbb69b1..ea465e2c834d072768cb20b70990981891771021 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -298,7 +298,7 @@ class SQLContext private[sql](val sparkSession: SparkSession)
}
/**
- * Convert a [[BaseRelation]] created for external data sources into a `DataFrame`.
+ * Convert a `BaseRelation` created for external data sources into a `DataFrame`.
*
* @group dataframes
* @since 1.3.0
@@ -309,7 +309,7 @@ class SQLContext private[sql](val sparkSession: SparkSession)
/**
* :: DeveloperApi ::
- * Creates a `DataFrame` from an [[RDD]] containing [[Row]]s using the given schema.
+ * Creates a `DataFrame` from an `RDD` containing [[Row]]s using the given schema.
* It is important to make sure that the structure of every [[Row]] of the provided RDD matches
* the provided schema. Otherwise, there will be runtime exception.
* Example:
@@ -406,7 +406,7 @@ class SQLContext private[sql](val sparkSession: SparkSession)
/**
* :: Experimental ::
- * Creates a [[Dataset]] from a [[java.util.List]] of a given type. This method requires an
+ * Creates a [[Dataset]] from a `java.util.List` of a given type. This method requires an
* encoder (to convert a JVM object of type `T` to and from the internal Spark SQL representation)
* that is generally created automatically through implicits from a `SparkSession`, or can be
* created explicitly by calling static methods on [[Encoders]].
@@ -438,7 +438,7 @@ class SQLContext private[sql](val sparkSession: SparkSession)
/**
* :: DeveloperApi ::
- * Creates a `DataFrame` from a [[JavaRDD]] containing [[Row]]s using the given schema.
+ * Creates a `DataFrame` from a `JavaRDD` containing [[Row]]s using the given schema.
* It is important to make sure that the structure of every [[Row]] of the provided RDD matches
* the provided schema. Otherwise, there will be runtime exception.
*
@@ -453,7 +453,7 @@ class SQLContext private[sql](val sparkSession: SparkSession)
/**
* :: DeveloperApi ::
- * Creates a `DataFrame` from a [[java.util.List]] containing [[Row]]s using the given schema.
+ * Creates a `DataFrame` from a `java.util.List` containing [[Row]]s using the given schema.
* It is important to make sure that the structure of every [[Row]] of the provided List matches
* the provided schema. Otherwise, there will be runtime exception.
*
@@ -518,7 +518,7 @@ class SQLContext private[sql](val sparkSession: SparkSession)
/**
* :: Experimental ::
- * Returns a [[DataStreamReader]] that can be used to read streaming data in as a `DataFrame`.
+ * Returns a `DataStreamReader` that can be used to read streaming data in as a `DataFrame`.
* {{{
* sparkSession.readStream.parquet("/path/to/directory/of/parquet/files")
* sparkSession.readStream.schema(schema).json("/path/to/directory/of/json/files")
@@ -638,7 +638,7 @@ class SQLContext private[sql](val sparkSession: SparkSession)
/**
* :: Experimental ::
- * Creates a `DataFrame` with a single [[LongType]] column named `id`, containing elements
+ * Creates a `DataFrame` with a single `LongType` column named `id`, containing elements
* in a range from 0 to `end` (exclusive) with step value 1.
*
* @since 1.4.1
@@ -650,7 +650,7 @@ class SQLContext private[sql](val sparkSession: SparkSession)
/**
* :: Experimental ::
- * Creates a `DataFrame` with a single [[LongType]] column named `id`, containing elements
+ * Creates a `DataFrame` with a single `LongType` column named `id`, containing elements
* in a range from `start` to `end` (exclusive) with step value 1.
*
* @since 1.4.0
@@ -662,7 +662,7 @@ class SQLContext private[sql](val sparkSession: SparkSession)
/**
* :: Experimental ::
- * Creates a `DataFrame` with a single [[LongType]] column named `id`, containing elements
+ * Creates a `DataFrame` with a single `LongType` column named `id`, containing elements
* in a range from `start` to `end` (exclusive) with a step value.
*
* @since 2.0.0
@@ -676,7 +676,7 @@ class SQLContext private[sql](val sparkSession: SparkSession)
/**
* :: Experimental ::
- * Creates a `DataFrame` with a single [[LongType]] column named `id`, containing elements
+ * Creates a `DataFrame` with a single `LongType` column named `id`, containing elements
* in an range from `start` to `end` (exclusive) with an step value, with partition number
* specified.
*
@@ -733,7 +733,7 @@ class SQLContext private[sql](val sparkSession: SparkSession)
}
/**
- * Returns a [[StreamingQueryManager]] that allows managing all the
+ * Returns a `StreamingQueryManager` that allows managing all the
* [[org.apache.spark.sql.streaming.StreamingQuery StreamingQueries]] active on `this` context.
*
* @since 2.0.0
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala b/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala
index f3dde480eabe03223616693479a543c0cf5e704a..e1fdb2f2876b9f9d82344dfd6f65c7d799a45d8a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala
@@ -323,7 +323,7 @@ class SparkSession private(
/**
* :: DeveloperApi ::
- * Creates a `DataFrame` from a [[java.util.List]] containing [[Row]]s using the given schema.
+ * Creates a `DataFrame` from a `java.util.List` containing [[Row]]s using the given schema.
* It is important to make sure that the structure of every [[Row]] of the provided List matches
* the provided schema. Otherwise, there will be runtime exception.
*
@@ -448,7 +448,7 @@ class SparkSession private(
/**
* :: Experimental ::
- * Creates a [[Dataset]] from a [[java.util.List]] of a given type. This method requires an
+ * Creates a [[Dataset]] from a `java.util.List` of a given type. This method requires an
* encoder (to convert a JVM object of type `T` to and from the internal Spark SQL representation)
* that is generally created automatically through implicits from a `SparkSession`, or can be
* created explicitly by calling static methods on [[Encoders]].
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index c86ae5be9ef62f440d0a9d2f99eef838ab111eaa..5e27484c110473a81f1eb03b669ba0824449c493 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1200,8 +1200,8 @@ object functions {
* Creates a new struct column.
* If the input column is a column in a `DataFrame`, or a derived column expression
* that is named (i.e. aliased), its name would be remained as the StructField's name,
- * otherwise, the newly generated StructField's name would be auto generated as col${index + 1},
- * i.e. col1, col2, col3, ...
+ * otherwise, the newly generated StructField's name would be auto generated as
+ * `col` with a suffix `index + 1`, i.e. col1, col2, col3, ...
*
* @group normal_funcs
* @since 1.4.0
@@ -2482,7 +2482,7 @@ object functions {
* format given by the second argument.
*
* A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All
- * pattern letters of [[java.text.SimpleDateFormat]] can be used.
+ * pattern letters of `java.text.SimpleDateFormat` can be used.
*
* @note Use when ever possible specialized functions like [[year]]. These benefit from a
* specialized implementation.
@@ -2732,14 +2732,14 @@ object functions {
* @param timeColumn The column or the expression to use as the timestamp for windowing by time.
* The time column must be of TimestampType.
* @param windowDuration A string specifying the width of the window, e.g. `10 minutes`,
- * `1 second`. Check [[org.apache.spark.unsafe.types.CalendarInterval]] for
+ * `1 second`. Check `org.apache.spark.unsafe.types.CalendarInterval` for
* valid duration identifiers. Note that the duration is a fixed length of
* time, and does not vary over time according to a calendar. For example,
* `1 day` always means 86,400,000 milliseconds, not a calendar day.
* @param slideDuration A string specifying the sliding interval of the window, e.g. `1 minute`.
* A new window will be generated every `slideDuration`. Must be less than
* or equal to the `windowDuration`. Check
- * [[org.apache.spark.unsafe.types.CalendarInterval]] for valid duration
+ * `org.apache.spark.unsafe.types.CalendarInterval` for valid duration
* identifiers. This duration is likewise absolute, and does not vary
* according to a calendar.
* @param startTime The offset with respect to 1970-01-01 00:00:00 UTC with which to start
@@ -2790,14 +2790,14 @@ object functions {
* @param timeColumn The column or the expression to use as the timestamp for windowing by time.
* The time column must be of TimestampType.
* @param windowDuration A string specifying the width of the window, e.g. `10 minutes`,
- * `1 second`. Check [[org.apache.spark.unsafe.types.CalendarInterval]] for
+ * `1 second`. Check `org.apache.spark.unsafe.types.CalendarInterval` for
* valid duration identifiers. Note that the duration is a fixed length of
* time, and does not vary over time according to a calendar. For example,
* `1 day` always means 86,400,000 milliseconds, not a calendar day.
* @param slideDuration A string specifying the sliding interval of the window, e.g. `1 minute`.
* A new window will be generated every `slideDuration`. Must be less than
* or equal to the `windowDuration`. Check
- * [[org.apache.spark.unsafe.types.CalendarInterval]] for valid duration
+ * `org.apache.spark.unsafe.types.CalendarInterval` for valid duration
* identifiers. This duration is likewise absolute, and does not vary
* according to a calendar.
*
@@ -2837,7 +2837,7 @@ object functions {
* @param timeColumn The column or the expression to use as the timestamp for windowing by time.
* The time column must be of TimestampType.
* @param windowDuration A string specifying the width of the window, e.g. `10 minutes`,
- * `1 second`. Check [[org.apache.spark.unsafe.types.CalendarInterval]] for
+ * `1 second`. Check `org.apache.spark.unsafe.types.CalendarInterval` for
* valid duration identifiers.
*
* @group datetime_funcs
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index f288ad61410f7cd97060a2fb0376b88ea38cd1fc..ff8b15b3ff3fff1dfdc42988aca7453af1f471d2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -305,7 +305,7 @@ trait InsertableRelation {
* ::Experimental::
* An interface for experimenting with a more direct connection to the query planner. Compared to
* [[PrunedFilteredScan]], this operator receives the raw expressions from the
- * [[org.apache.spark.sql.catalyst.plans.logical.LogicalPlan]]. Unlike the other APIs this
+ * `org.apache.spark.sql.catalyst.plans.logical.LogicalPlan`. Unlike the other APIs this
* interface is NOT designed to be binary compatible across releases and thus should only be used
* for experimentation.
*
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamReader.scala
index 6d2cede6f5a23d4d1454dea7db434a09912487fe..b7ffb3cddb472ce8d1d2bbcdefaa112841a8a556 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamReader.scala
@@ -192,7 +192,7 @@ final class DataStreamReader private[sql](sparkSession: SparkSession) extends Lo
*
* This function will go through the input once to determine the input schema if `inferSchema`
* is enabled. To avoid going through the entire data once, disable `inferSchema` option or
- * specify the schema explicitly using [[schema]].
+ * specify the schema explicitly using `schema`.
*
* You can set the following CSV-specific options to deal with CSV files:
* <ul>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/streaming/StreamingQuery.scala b/sql/core/src/main/scala/org/apache/spark/sql/streaming/StreamingQuery.scala
index 9c00259f73e2ea94fd75b692fa8cd46644a8ff2e..12a1bb1db5779c9c70a08816ee5d5d85d00a4aa8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/streaming/StreamingQuery.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/streaming/StreamingQuery.scala
@@ -137,7 +137,7 @@ trait StreamingQuery {
* Blocks until all available data in the source has been processed and committed to the sink.
* This method is intended for testing. Note that in the case of continually arriving data, this
* method may block forever. Additionally, this method is only guaranteed to block until data that
- * has been synchronously appended data to a [[org.apache.spark.sql.execution.streaming.Source]]
+ * has been synchronously appended data to a `org.apache.spark.sql.execution.streaming.Source`
* prior to invocation. (i.e. `getOffset` must immediately reflect the addition).
* @since 2.0.0
*/
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/streaming/StreamingQueryListener.scala b/sql/core/src/main/scala/org/apache/spark/sql/streaming/StreamingQueryListener.scala
index 6b871b1fe68577f0d4f0f3d8a5f38c9778c189db..c376913516ef73765f4b5ae3816a9504f5de25b0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/streaming/StreamingQueryListener.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/streaming/StreamingQueryListener.scala
@@ -52,7 +52,7 @@ abstract class StreamingQueryListener {
* @note This method is asynchronous. The status in [[StreamingQuery]] will always be
* latest no matter when this method is called. Therefore, the status of [[StreamingQuery]]
* may be changed before/when you process the event. E.g., you may find [[StreamingQuery]]
- * is terminated when you are processing [[QueryProgressEvent]].
+ * is terminated when you are processing `QueryProgressEvent`.
* @since 2.0.0
*/
def onQueryProgress(event: QueryProgressEvent): Unit
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 52aa1088acd4aa1b3c47c941b59bfefd1a422527..21b729bf29ea6bfb9e5ce23f25ebaba5f336f53c 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -772,7 +772,7 @@ private[hive] trait HiveInspectors {
/**
* Map the catalyst expression to ObjectInspector, however,
- * if the expression is [[Literal]] or foldable, a constant writable object inspector returns;
+ * if the expression is `Literal` or foldable, a constant writable object inspector returns;
* Otherwise, we always get the object inspector according to its data type(in catalyst)
* @param expr Catalyst expression to be mapped
* @return Hive java objectinspector (recursively).
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index badccae16837bc0d306aebd18c59986ff40ecdda..39be41770d8edb566c0cb4deb5ae024e441a86e6 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -80,7 +80,7 @@ class DetermineHiveSerde(conf: SQLConf) extends Rule[LogicalPlan] {
/**
* Replaces generic operations with specific variants that are designed to work with Hive.
*
- * Note that, this rule must be run after [[PreprocessTableInsertion]].
+ * Note that, this rule must be run after `PreprocessTableInsertion`.
*/
class HiveAnalysis(session: SparkSession) extends Rule[LogicalPlan] {
override def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
index ce418ae135dd9d09b4d45f373174ce92b7ebc0ea..3e654d8eeb3554101fe5ce4d511ff1259ad47d46 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -50,10 +50,10 @@ import org.apache.spark.SparkException
* blame Reynold for this! He was just moving code around!
*
* In the future we should converge the write path for Hive with the normal data source write path,
- * as defined in [[org.apache.spark.sql.execution.datasources.FileFormatWriter]].
+ * as defined in `org.apache.spark.sql.execution.datasources.FileFormatWriter`.
*
* @param table the logical plan representing the table. In the future this should be a
- * [[org.apache.spark.sql.catalyst.catalog.CatalogTable]] once we converge Hive tables
+ * `org.apache.spark.sql.catalyst.catalog.CatalogTable` once we converge Hive tables
* and data source tables.
* @param partition a map from the partition key to the partition value (optional). If the partition
* value is optional, dynamic partition insert will be performed.
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/State.scala b/streaming/src/main/scala/org/apache/spark/streaming/State.scala
index 23cf48eb0673839a38fe22ed2ffbeadbdce65a64..734c6ef42696ea931bc799fe61661b6d4ec52c0b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/State.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/State.scala
@@ -120,7 +120,7 @@ sealed abstract class State[S] {
def isTimingOut(): Boolean
/**
- * Get the state as a [[scala.Option]]. It will be `Some(state)` if it exists, otherwise `None`.
+ * Get the state as a `scala.Option`. It will be `Some(state)` if it exists, otherwise `None`.
*/
@inline final def getOption(): Option[S] = if (exists) Some(get()) else None