variable naming

apache · Dec 27, 2016 · f2dbdd9 · f2dbdd9
1 parent 9f2eaf3
commit f2dbdd9
Showing 1 changed file with 6 additions and 6 deletions.
diff --git a/mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala b/mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala
@@ -210,12 +210,12 @@ object MLTestingUtils extends SparkFunSuite {
    * to assigning a sample weight proportional to the number of samples for each point.
    */
   def testOversamplingVsWeighting[M <: Model[M], E <: Estimator[M]](
-      df: Dataset[LabeledPoint],
+      data: Dataset[LabeledPoint],
       estimator: E with HasWeightCol,
       modelEquals: (M, M) => Unit,
       seed: Long): Unit = {
     val (overSampledData, weightedData) = genEquivalentOversampledAndWeightedInstances(
-      df, seed)
+      data, seed)
     val weightedModel = estimator.set(estimator.weightCol, "weight").fit(weightedData)
     val overSampledModel = estimator.set(estimator.weightCol, "").fit(overSampledData)
     modelEquals(weightedModel, overSampledModel)
@@ -227,17 +227,17 @@ object MLTestingUtils extends SparkFunSuite {
    * model despite the outliers.
    */
   def testOutliersWithSmallWeights[M <: Model[M], E <: Estimator[M]](
-      ds: Dataset[LabeledPoint],
+      data: Dataset[LabeledPoint],
       estimator: E with HasWeightCol,
       numClasses: Int,
       modelEquals: (M, M) => Unit): Unit = {
-    import ds.sqlContext.implicits._
-    val outlierDS = ds.withColumn("weight", lit(1.0)).as[Instance].flatMap {
+    import data.sqlContext.implicits._
+    val outlierDS = data.withColumn("weight", lit(1.0)).as[Instance].flatMap {
       case Instance(l, w, f) =>
         val outlierLabel = if (numClasses == 0) -l else numClasses - l - 1
         List.fill(3)(Instance(outlierLabel, 0.0001, f)) ++ List(Instance(l, w, f))
     }
-    val trueModel = estimator.set(estimator.weightCol, "").fit(ds)
+    val trueModel = estimator.set(estimator.weightCol, "").fit(data)
     val outlierModel = estimator.set(estimator.weightCol, "weight").fit(outlierDS)
     modelEquals(trueModel, outlierModel)
   }