[SPARK-4256] Update Code formatting for BinaryClassificationMetricsSpec

Author: Andrew Bullen

mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala

@@ -30,31 +30,36 @@ class BinaryClassificationMetricsSuite extends FunSuite with LocalSparkContext {
     (x._1._1 ~= x._2._1 absTol 1E-5) && (x._1._2 ~= x._2._2 absTol 1E-5)
 
   private def assertSequencesMatch(left: Seq[Double], right: Seq[Double]): Unit = {
-    assert(left.zip(right).forall(areWithinEpsilon))
+      assert(left.zip(right).forall(areWithinEpsilon))
   }
 
-  private def assertTupleSequencesMatch(left: Seq[(Double, Double)], right: Seq[(Double, Double)]): Unit = {
+  private def assertTupleSequencesMatch(left: Seq[(Double, Double)],
+       right: Seq[(Double, Double)]): Unit = {
     assert(left.zip(right).forall(pairsWithinEpsilon))
   }
 
   private def validateMetrics(metrics: BinaryClassificationMetrics,
-                              expectedThresholds: Seq[Double],
-                              expectedROCCurve:   Seq[(Double, Double)],
-                              expectedPRCurve:    Seq[(Double, Double)],
-                              expectedFMeasures1: Seq[Double],
-                              expectedFmeasures2: Seq[Double],
-                              expectedPrecisions: Seq[Double],
-                              expectedRecalls:    Seq[Double]) = {
+      expectedThresholds: Seq[Double],
+      expectedROCCurve: Seq[(Double, Double)],
+      expectedPRCurve: Seq[(Double, Double)],
+      expectedFMeasures1: Seq[Double],
+      expectedFmeasures2: Seq[Double],
+      expectedPrecisions: Seq[Double],
+      expectedRecalls: Seq[Double]) = {
 
     assertSequencesMatch(metrics.thresholds().collect(), expectedThresholds)
     assertTupleSequencesMatch(metrics.roc().collect(), expectedROCCurve)
     assert(metrics.areaUnderROC() ~== AreaUnderCurve.of(expectedROCCurve) absTol 1E-5)
     assertTupleSequencesMatch(metrics.pr().collect(), expectedPRCurve)
     assert(metrics.areaUnderPR() ~== AreaUnderCurve.of(expectedPRCurve) absTol 1E-5)
-    assertTupleSequencesMatch(metrics.fMeasureByThreshold().collect(), expectedThresholds.zip(expectedFMeasures1))
-    assertTupleSequencesMatch(metrics.fMeasureByThreshold(2.0).collect(), expectedThresholds.zip(expectedFmeasures2))
-    assertTupleSequencesMatch(metrics.precisionByThreshold().collect(), expectedThresholds.zip(expectedPrecisions))
-    assertTupleSequencesMatch(metrics.recallByThreshold().collect(), expectedThresholds.zip(expectedRecalls))
+    assertTupleSequencesMatch(metrics.fMeasureByThreshold().collect(),
+      expectedThresholds.zip(expectedFMeasures1))
+    assertTupleSequencesMatch(metrics.fMeasureByThreshold(2.0).collect(),
+      expectedThresholds.zip(expectedFmeasures2))
+    assertTupleSequencesMatch(metrics.precisionByThreshold().collect(),
+      expectedThresholds.zip(expectedPrecisions))
+    assertTupleSequencesMatch(metrics.recallByThreshold().collect(),
+      expectedThresholds.zip(expectedRecalls))
   }
 
   test("binary evaluation metrics") {
@@ -80,9 +85,9 @@ class BinaryClassificationMetricsSuite extends FunSuite with LocalSparkContext {
     validateMetrics(metrics, thresholds, rocCurve, prCurve, f1, f2, precisions, recalls)
   }
 
-  test("binary evaluation metrics for All Positive RDD") {
+  test("binary evaluation metrics for RDD where all examples have positive label") {
     val scoreAndLabels = sc.parallelize(Seq((0.5, 1.0), (0.5, 1.0)), 2)
-    val metrics: BinaryClassificationMetrics = new BinaryClassificationMetrics(scoreAndLabels)
+    val metrics = new BinaryClassificationMetrics(scoreAndLabels)
 
     val thresholds = Seq(0.5)
     val precisions = Seq(1.0)
@@ -97,9 +102,9 @@ class BinaryClassificationMetricsSuite extends FunSuite with LocalSparkContext {
     validateMetrics(metrics, thresholds, rocCurve, prCurve, f1, f2, precisions, recalls)
   }
 
-  test("binary evaluation metrics for All Negative RDD") {
+  test("binary evaluation metrics for RDD where all examples have negative label") {
     val scoreAndLabels = sc.parallelize(Seq((0.5, 0.0), (0.5, 0.0)), 2)
-    val metrics: BinaryClassificationMetrics = new BinaryClassificationMetrics(scoreAndLabels)
+    val metrics = new BinaryClassificationMetrics(scoreAndLabels)
 
     val thresholds = Seq(0.5)
     val precisions = Seq(0.0)