1 files changed, 5 insertions, 8 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 0d329d2c08..ac0124513f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -263,11 +263,10 @@ class LogisticRegression @Since("1.2.0") (
   protected[spark] def train(dataset: DataFrame, handlePersistence: Boolean):
       LogisticRegressionModel = {
     val w = if ($(weightCol).isEmpty) lit(1.0) else col($(weightCol))
-    val instances: RDD[Instance] =
-      dataset.select(col($(labelCol)), w, col($(featuresCol))).rdd.map {
-        case Row(label: Double, weight: Double, features: Vector) =>
-          Instance(label, weight, features)
-      }
+    val instances: RDD[Instance] = dataset.select(col($(labelCol)), w, col($(featuresCol))).map {
+      case Row(label: Double, weight: Double, features: Vector) =>
+        Instance(label, weight, features)
+    }
 
     if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
 
@@ -791,7 +790,6 @@ sealed trait LogisticRegressionSummary extends Serializable {
 /**
  * :: Experimental ::
  * Logistic regression training results.
- *
  * @param predictions dataframe outputted by the model's `transform` method.
  * @param probabilityCol field in "predictions" which gives the calibrated probability of
  *                       each instance as a vector.
@@ -815,7 +813,6 @@ class BinaryLogisticRegressionTrainingSummary private[classification] (
 /**
  * :: Experimental ::
  * Binary Logistic regression results for a given model.
- *
  * @param predictions dataframe outputted by the model's `transform` method.
  * @param probabilityCol field in "predictions" which gives the calibrated probability of
  *                       each instance.
@@ -840,7 +837,7 @@ class BinaryLogisticRegressionSummary private[classification] (
   // TODO: Allow the user to vary the number of bins using a setBins method in
   // BinaryClassificationMetrics. For now the default is set to 100.
   @transient private val binaryMetrics = new BinaryClassificationMetrics(
-    predictions.select(probabilityCol, labelCol).rdd.map {
+    predictions.select(probabilityCol, labelCol).map {
       case Row(score: Vector, label: Double) => (score(1), label)
     }, 100
   )