removed redundant import, fixed some style issues

dataset/src/test/scala/frameless/functions/AggregateFunctionsTests.scala

@@ -362,19 +362,22 @@ class AggregateFunctionsTests extends TypedDatasetSuite {
  test("corr") {
  val spark = session
  import spark.implicits._
- import frameless.CatalystCast._
 
  def prop[A: TypedEncoder, B: TypedEncoder](xs: List[X3[Int, A, B]])(
  implicit
- encEv:Encoder[(Int, A, B)],
- evCanBeDoubleA:CatalystCast[A, Double],
- evCanBeDoubleB:CatalystCast[B, Double]
+ encEv: Encoder[(Int, A, B)],
+ evCanBeDoubleA: CatalystCast[A, Double],
+ evCanBeDoubleB: CatalystCast[B, Double]
  ): Prop = {
 
  // mapping with this function is needed because spark uses Double.NaN for some semantics in the correlation function. ?= for prop testing will use == underlying and will break because Double.NaN != Double.NaN
- val nanHandler : Double => Option[Double] = value => if (!value.equals(Double.NaN)) Option(value) else None
+ val nanHandler: Double => Option[Double] = value => if (!value.equals(Double.NaN)) Option(value) else None
  //making sure that null => None and does not result in 0.0d because of row.getAs[Double]'s use of .asInstanceOf
- val nanNullHandler : Any => Option[Double] = value=> if (value != null) nanHandler(value.asInstanceOf[Double]) else None
+ val nanNullHandler: Any => Option[Double] = {
+ case null => None
+ case d: Double => nanHandler(d)
+ case _ => ???
+ }
 
  val tds = TypedDataset.create(xs)
  val tdCorrelation = tds.groupBy(tds('a)).agg(corr(tds('b), tds('c)))
@@ -384,7 +387,7 @@ class AggregateFunctionsTests extends TypedDatasetSuite {
 
 
 
- val cDF = spark.createDataset(xs.map(x => {(x.a,x.b,x.c)}))
+ val cDF = session.createDataset(xs.map(x => (x.a,x.b,x.c)))
  val compCorrelation = cDF
  .groupBy(cDF("_1"))
  .agg(org.apache.spark.sql.functions.corr(cDF("_2"), cDF("_3")))