update extends indentation

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

@@ -130,7 +130,8 @@ class Pipeline extends Estimator[PipelineModel] {
 class PipelineModel(
     override val parent: Pipeline,
     override val fittingParamMap: ParamMap,
-    val transformers: Array[Transformer]) extends Model[PipelineModel] with Logging {
+    val transformers: Array[Transformer])
+  extends Model[PipelineModel] with Logging {
 
   /**
    * Gets the model produced by the input estimator. Throws an NoSuchElementException is the input

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

@@ -84,7 +84,7 @@ abstract class Transformer extends PipelineStage with Params {
  * result as a new column.
  */
 abstract class UnaryTransformer[IN, OUT: TypeTag, T <: UnaryTransformer[IN, OUT, T]]
-    extends Transformer with HasInputCol with HasOutputCol with Logging {
+  extends Transformer with HasInputCol with HasOutputCol with Logging {
 
   def setInputCol(value: String): T = { set(inputCol, value); this.asInstanceOf[T] }
   def setOutputCol(value: String): T = { set(outputCol, value); this.asInstanceOf[T] }

mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala

@@ -113,7 +113,8 @@ class LogisticRegression extends Estimator[LogisticRegressionModel] with Logisti
 class LogisticRegressionModel private[ml] (
     override val parent: LogisticRegression,
     override val fittingParamMap: ParamMap,
-    val weights: Vector) extends Model[LogisticRegressionModel] with LogisticRegressionParams {
+    val weights: Vector)
+  extends Model[LogisticRegressionModel] with LogisticRegressionParams {
 
   def setThreshold(value: Double): this.type = { set(threshold, value); this }
   def setFeaturesCol(value: String): this.type = { set(featuresCol, value); this }

mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala

@@ -21,13 +21,12 @@ import org.apache.spark.ml._
 import org.apache.spark.ml.param._
 import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
 import org.apache.spark.sql.{DoubleType, Row, SchemaRDD}
-import org.apache.spark.storage.StorageLevel
 
 /**
  * Evaluator for binary classification, which expects two input columns: score and label.
  */
 class BinaryClassificationEvaluator extends Evaluator with Params
-    with HasScoreCol with HasLabelCol {
+  with HasScoreCol with HasLabelCol {
 
   /** param for metric name in evaluation */
   val metricName: Param[String] = new Param(this, "metricName",

mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala

@@ -71,8 +71,8 @@ class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerP
 class StandardScalerModel private[ml] (
     override val parent: StandardScaler,
     override val fittingParamMap: ParamMap,
-    scaler: feature.StandardScalerModel) extends Model[StandardScalerModel]
-  with StandardScalerParams {
+    scaler: feature.StandardScalerModel)
+  extends Model[StandardScalerModel] with StandardScalerParams {
 
   def setInputCol(value: String): this.type = { set(inputCol, value); this }
   def setOutputCol(value: String): this.type = { set(outputCol, value); this }

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

@@ -37,7 +37,8 @@ class Param[T] (
     val parent: Params,
     val name: String,
     val doc: String,
-    val defaultValue: Option[T] = None) extends Serializable {
+    val defaultValue: Option[T] = None)
+  extends Serializable {
 
   /**
    * Creates a param pair with the given value (for Java).
@@ -62,31 +63,36 @@ class Param[T] (
 
 /** Specialized version of [[Param[Double]]] for Java. */
 class DoubleParam(parent: Params, name: String, doc: String, defaultValue: Option[Double] = None)
-    extends Param[Double](parent, name, doc, defaultValue) {
+  extends Param[Double](parent, name, doc, defaultValue) {
+
   override def w(value: Double): ParamPair[Double] = super.w(value)
 }
 
 /** Specialized version of [[Param[Int]]] for Java. */
 class IntParam(parent: Params, name: String, doc: String, defaultValue: Option[Int] = None)
-    extends Param[Int](parent, name, doc, defaultValue) {
+  extends Param[Int](parent, name, doc, defaultValue) {
+
   override def w(value: Int): ParamPair[Int] = super.w(value)
 }
 
 /** Specialized version of [[Param[Float]]] for Java. */
 class FloatParam(parent: Params, name: String, doc: String, defaultValue: Option[Float] = None)
-    extends Param[Float](parent, name, doc, defaultValue) {
+  extends Param[Float](parent, name, doc, defaultValue) {
+
   override def w(value: Float): ParamPair[Float] = super.w(value)
 }
 
 /** Specialized version of [[Param[Long]]] for Java. */
 class LongParam(parent: Params, name: String, doc: String, defaultValue: Option[Long] = None)
-    extends Param[Long](parent, name, doc, defaultValue) {
+  extends Param[Long](parent, name, doc, defaultValue) {
+
   override def w(value: Long): ParamPair[Long] = super.w(value)
 }
 
 /** Specialized version of [[Param[Boolean]]] for Java. */
 class BooleanParam(parent: Params, name: String, doc: String, defaultValue: Option[Boolean] = None)
-    extends Param[Boolean](parent, name, doc, defaultValue) {
+  extends Param[Boolean](parent, name, doc, defaultValue) {
+
   override def w(value: Boolean): ParamPair[Boolean] = super.w(value)
 }
 

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

@@ -111,7 +111,8 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP
 class CrossValidatorModel private[ml] (
     override val parent: CrossValidator,
     override val fittingParamMap: ParamMap,
-    val bestModel: Model[_]) extends Model[CrossValidatorModel] with CrossValidatorParams {
+    val bestModel: Model[_])
+  extends Model[CrossValidatorModel] with CrossValidatorParams {
 
   override def transform(dataset: SchemaRDD, paramMap: ParamMap): SchemaRDD = {
     bestModel.transform(dataset, paramMap)