[SPARK-3854][BUILD] Scala style: require spaces before `{`.

## What changes were proposed in this pull request?

Since the opening curly brace, '{', has many usages as discussed in [SPARK-3854](https://issues.apache.org/jira/browse/SPARK-3854), this PR adds a ScalaStyle rule to prevent '){' pattern  for the following majority pattern and fixes the code accordingly. If we enforce this in ScalaStyle from now, it will improve the Scala code quality and reduce review time.
```
// Correct:
if (true) {
  println("Wow!")
}

// Incorrect:
if (true){
   println("Wow!")
}
```
IntelliJ also shows new warnings based on this.

## How was this patch tested?

Pass the Jenkins ScalaStyle test.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #11637 from dongjoon-hyun/SPARK-3854.

11 files changed, 15 insertions, 15 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala b/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
index d02806a6ea..f21b623e93 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
@@ -213,8 +213,8 @@ private[ann] object AffineLayerModel {
    */
   def randomWeights(numIn: Int, numOut: Int, seed: Long = 11L): (BDM[Double], BDV[Double]) = {
     val rand: XORShiftRandom = new XORShiftRandom(seed)
-    val weights = BDM.fill[Double](numOut, numIn){ (rand.nextDouble * 4.8 - 2.4) / numIn }
-    val bias = BDV.fill[Double](numOut){ (rand.nextDouble * 4.8 - 2.4) / numIn }
+    val weights = BDM.fill[Double](numOut, numIn) { (rand.nextDouble * 4.8 - 2.4) / numIn }
+    val bias = BDV.fill[Double](numOut) { (rand.nextDouble * 4.8 - 2.4) / numIn }
     (weights, bias)
   }
 }
@@ -529,7 +529,7 @@ private[ml] object FeedForwardTopology {
    */
   def multiLayerPerceptron(layerSizes: Array[Int], softmax: Boolean = true): FeedForwardTopology = {
     val layers = new Array[Layer]((layerSizes.length - 1) * 2)
-    for(i <- 0 until layerSizes.length - 1){
+    for(i <- 0 until layerSizes.length - 1) {
       layers(i * 2) = new AffineLayer(layerSizes(i), layerSizes(i + 1))
       layers(i * 2 + 1) =
         if (softmax && i == layerSizes.length - 2) {
@@ -655,7 +655,7 @@ private[ann] object FeedForwardModel {
     val layers = topology.layers
     val layerModels = new Array[LayerModel](layers.length)
     var offset = 0
-    for(i <- 0 until layers.length){
+    for(i <- 0 until layers.length) {
       layerModels(i) = layers(i).getInstance(seed)
       offset += layerModels(i).size
     }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
index 718f49d3ae..483ef0d88c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
@@ -145,7 +145,7 @@ class NaiveBayesModel private[ml] (
     case Multinomial => (None, None)
     case Bernoulli =>
       val negTheta = theta.map(value => math.log(1.0 - math.exp(value)))
-      val ones = new DenseVector(Array.fill(theta.numCols){1.0})
+      val ones = new DenseVector(Array.fill(theta.numCols) {1.0})
       val thetaMinusNegTheta = theta.map { value =>
         value - math.log(1.0 - math.exp(value))
       }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index 886cd60687..132dc174a8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -428,7 +428,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       val weight = wt.toArray
       val mean = mu.map(_.asInstanceOf[DenseVector])
       val sigma = si.map(_.asInstanceOf[DenseMatrix])
-      val gaussians = Array.tabulate(weight.length){
+      val gaussians = Array.tabulate(weight.length) {
         i => new MultivariateGaussian(mean(i), sigma(i))
       }
       val model = new GaussianMixtureModel(weight, gaussians)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
index aef9ef2cb0..9026b97f1c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
@@ -74,7 +74,7 @@ class NaiveBayesModel private[spark] (
     case Multinomial => (None, None)
     case Bernoulli =>
       val negTheta = thetaMatrix.map(value => math.log(1.0 - math.exp(value)))
-      val ones = new DenseVector(Array.fill(thetaMatrix.numCols){1.0})
+      val ones = new DenseVector(Array.fill(thetaMatrix.numCols) {1.0})
       val thetaMinusNegTheta = thetaMatrix.map { value =>
         value - math.log(1.0 - math.exp(value))
       }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
index 6dd541e5c0..77bd0aa30d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
@@ -152,7 +152,7 @@ class MultilabelMetrics @Since("1.2.0") (predictionAndLabels: RDD[(Array[Double]
    */
   @Since("1.2.0")
   lazy val microPrecision: Double = {
-    val sumFp = fpPerClass.foldLeft(0L){ case(cum, (_, fp)) => cum + fp}
+    val sumFp = fpPerClass.foldLeft(0L) { case(cum, (_, fp)) => cum + fp}
     sumTp.toDouble / (sumTp + sumFp)
   }
 
@@ -162,7 +162,7 @@ class MultilabelMetrics @Since("1.2.0") (predictionAndLabels: RDD[(Array[Double]
    */
   @Since("1.2.0")
   lazy val microRecall: Double = {
-    val sumFn = fnPerClass.foldLeft(0.0){ case(cum, (_, fn)) => cum + fn}
+    val sumFn = fnPerClass.foldLeft(0.0) { case(cum, (_, fn)) => cum + fn}
     sumTp.toDouble / (sumTp + sumFn)
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
index d2687dc11b..27a7380567 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
@@ -420,7 +420,7 @@ private[spark] object BLAS extends Serializable with Logging {
     val AcolPtrs = A.colPtrs
 
     // Slicing is easy in this case. This is the optimal multiplication setting for sparse matrices
-    if (A.isTransposed){
+    if (A.isTransposed) {
       var colCounterForB = 0
       if (!B.isTransposed) { // Expensive to put the check inside the loop
         while (colCounterForB < nB) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
index f235089873..abdd798197 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
@@ -136,7 +136,7 @@ class IsotonicRegressionModel @Since("1.3.0") (
     // higher than all values, in between two values or exact match.
     if (insertIndex == 0) {
       predictions.head
-    } else if (insertIndex == boundaries.length){
+    } else if (insertIndex == boundaries.length) {
       predictions.last
     } else if (foundIndex < 0) {
       linearInterpolation(
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala
index a841c5caf0..2c613348c2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala
@@ -98,7 +98,7 @@ private[mllib] object NumericParser {
         }
       } else if (token == ")") {
         parsing = false
-      } else if (token.trim.isEmpty){
+      } else if (token.trim.isEmpty) {
           // ignore whitespaces between delim chars, e.g. ", ["
       } else {
         // expecting a number
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
index 6b810ab9ea..4c7c56782c 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
@@ -105,7 +105,7 @@ class RandomForestClassifierSuite extends SparkFunSuite with MLlibTestSparkConte
     compareAPIs(rdd, rf, categoricalFeatures, numClasses)
   }
 
-  test("subsampling rate in RandomForest"){
+  test("subsampling rate in RandomForest") {
     val rdd = orderedLabeledPoints5_20
     val categoricalFeatures = Map.empty[Int, Int]
     val numClasses = 2
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala
index fb3bd3f412..67e680be73 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala
@@ -182,7 +182,7 @@ class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext {
       Vectors.dense( 4.5605), Vectors.dense( 5.2043), Vectors.dense( 6.2734)
     )
 
-    val data2: Array[Vector] = Array.tabulate(25){ i: Int =>
+    val data2: Array[Vector] = Array.tabulate(25) { i: Int =>
       Vectors.dense(Array.tabulate(50)(i + _.toDouble))
     }
 
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala
index e6df5d974b..c72fc9bb4f 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala
@@ -197,7 +197,7 @@ class RandomForestSuite extends SparkFunSuite with MLlibTestSparkContext {
       featureSubsetStrategy = "sqrt", seed = 12345)
   }
 
-  test("subsampling rate in RandomForest"){
+  test("subsampling rate in RandomForest") {
     val arr = EnsembleTestHelper.generateOrderedLabeledPoints(5, 20)
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 2,