Remove simple redundant return statement for Scala methods/functions:

-) Only change simple return statements at the end of method
-) Ignore the complex if-else check
-) Ignore the ones inside synchronized

1 files changed, 14 insertions, 15 deletions

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 2d8623392e..c972a71349 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
@@ -48,7 +48,7 @@ class PythonMLLibAPI extends Serializable {
     val db = bb.asDoubleBuffer()
     val ans = new Array[Double](length.toInt)
     db.get(ans)
-    return ans
+    ans
   }
 
   private def serializeDoubleVector(doubles: Array[Double]): Array[Byte] = {
@@ -60,7 +60,7 @@ class PythonMLLibAPI extends Serializable {
     bb.putLong(len)
     val db = bb.asDoubleBuffer()
     db.put(doubles)
-    return bytes
+    bytes
   }
 
   private def deserializeDoubleMatrix(bytes: Array[Byte]): Array[Array[Double]] = {
@@ -86,7 +86,7 @@ class PythonMLLibAPI extends Serializable {
       ans(i) = new Array[Double](cols.toInt)
       db.get(ans(i))
     }
-    return ans
+    ans
   }
 
   private def serializeDoubleMatrix(doubles: Array[Array[Double]]): Array[Byte] = {
@@ -102,11 +102,10 @@ class PythonMLLibAPI extends Serializable {
     bb.putLong(rows)
     bb.putLong(cols)
     val db = bb.asDoubleBuffer()
-    var i = 0
     for (i <- 0 until rows) {
       db.put(doubles(i))
     }
-    return bytes
+    bytes
   }
 
   private def trainRegressionModel(trainFunc: (RDD[LabeledPoint], Array[Double]) => GeneralizedLinearModel,
@@ -121,7 +120,7 @@ class PythonMLLibAPI extends Serializable {
     val ret = new java.util.LinkedList[java.lang.Object]()
     ret.add(serializeDoubleVector(model.weights))
     ret.add(model.intercept: java.lang.Double)
-    return ret
+    ret
   }
 
   /**
@@ -130,7 +129,7 @@ class PythonMLLibAPI extends Serializable {
   def trainLinearRegressionModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]],
       numIterations: Int, stepSize: Double, miniBatchFraction: Double,
       initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = {
-    return trainRegressionModel((data, initialWeights) =>
+    trainRegressionModel((data, initialWeights) =>
         LinearRegressionWithSGD.train(data, numIterations, stepSize,
                                       miniBatchFraction, initialWeights),
         dataBytesJRDD, initialWeightsBA)
@@ -142,7 +141,7 @@ class PythonMLLibAPI extends Serializable {
   def trainLassoModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], numIterations: Int,
       stepSize: Double, regParam: Double, miniBatchFraction: Double,
       initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = {
-    return trainRegressionModel((data, initialWeights) =>
+    trainRegressionModel((data, initialWeights) =>
         LassoWithSGD.train(data, numIterations, stepSize, regParam,
                            miniBatchFraction, initialWeights),
         dataBytesJRDD, initialWeightsBA)
@@ -154,7 +153,7 @@ class PythonMLLibAPI extends Serializable {
   def trainRidgeModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], numIterations: Int,
       stepSize: Double, regParam: Double, miniBatchFraction: Double,
       initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = {
-    return trainRegressionModel((data, initialWeights) =>
+    trainRegressionModel((data, initialWeights) =>
         RidgeRegressionWithSGD.train(data, numIterations, stepSize, regParam,
                                      miniBatchFraction, initialWeights),
         dataBytesJRDD, initialWeightsBA)
@@ -166,7 +165,7 @@ class PythonMLLibAPI extends Serializable {
   def trainSVMModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], numIterations: Int,
       stepSize: Double, regParam: Double, miniBatchFraction: Double,
       initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = {
-    return trainRegressionModel((data, initialWeights) =>
+    trainRegressionModel((data, initialWeights) =>
         SVMWithSGD.train(data, numIterations, stepSize, regParam,
                                      miniBatchFraction, initialWeights),
         dataBytesJRDD, initialWeightsBA)
@@ -178,7 +177,7 @@ class PythonMLLibAPI extends Serializable {
   def trainLogisticRegressionModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]],
       numIterations: Int, stepSize: Double, miniBatchFraction: Double,
       initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = {
-    return trainRegressionModel((data, initialWeights) =>
+    trainRegressionModel((data, initialWeights) =>
         LogisticRegressionWithSGD.train(data, numIterations, stepSize,
                                      miniBatchFraction, initialWeights),
         dataBytesJRDD, initialWeightsBA)
@@ -194,7 +193,7 @@ class PythonMLLibAPI extends Serializable {
     val model = KMeans.train(data, k, maxIterations, runs, initializationMode)
     val ret = new java.util.LinkedList[java.lang.Object]()
     ret.add(serializeDoubleMatrix(model.clusterCenters))
-    return ret
+    ret
   }
 
   /** Unpack a Rating object from an array of bytes */
@@ -204,7 +203,7 @@ class PythonMLLibAPI extends Serializable {
     val user = bb.getInt()
     val product = bb.getInt()
     val rating = bb.getDouble()
-    return new Rating(user, product, rating)
+    new Rating(user, product, rating)
   }
 
   /** Unpack a tuple of Ints from an array of bytes */
@@ -245,7 +244,7 @@ class PythonMLLibAPI extends Serializable {
   def trainALSModel(ratingsBytesJRDD: JavaRDD[Array[Byte]], rank: Int,
       iterations: Int, lambda: Double, blocks: Int): MatrixFactorizationModel = {
     val ratings = ratingsBytesJRDD.rdd.map(unpackRating)
-    return ALS.train(ratings, rank, iterations, lambda, blocks)
+    ALS.train(ratings, rank, iterations, lambda, blocks)
   }
 
   /**
@@ -257,6 +256,6 @@ class PythonMLLibAPI extends Serializable {
   def trainImplicitALSModel(ratingsBytesJRDD: JavaRDD[Array[Byte]], rank: Int,
       iterations: Int, lambda: Double, blocks: Int, alpha: Double): MatrixFactorizationModel = {
     val ratings = ratingsBytesJRDD.rdd.map(unpackRating)
-    return ALS.trainImplicit(ratings, rank, iterations, lambda, blocks, alpha)
+    ALS.trainImplicit(ratings, rank, iterations, lambda, blocks, alpha)
   }
 }