add cache back

Author: mengxr

mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala

@@ -120,7 +120,7 @@ class IsotonicRegressionSuite extends FunSuite with MLlibTestSparkContext with M
   }
 
   test("isotonic regression with unordered input") {
-    val trainRDD = sc.parallelize(generateIsotonicInput(Seq(1, 2, 3, 4, 5)).reverse, 2)
+    val trainRDD = sc.parallelize(generateIsotonicInput(Seq(1, 2, 3, 4, 5)).reverse, 2).cache()
 
     val model = new IsotonicRegression().run(trainRDD)
     assert(model.predictions === Array(1, 2, 3, 4, 5))
@@ -169,7 +169,7 @@ class IsotonicRegressionSuite extends FunSuite with MLlibTestSparkContext with M
   test("isotonic regression prediction with duplicate features") {
     val trainRDD = sc.parallelize(
       Seq[(Double, Double, Double)](
-        (2, 1, 1), (1, 1, 1), (4, 2, 1), (2, 2, 1), (6, 3, 1), (5, 3, 1)), 2)
+        (2, 1, 1), (1, 1, 1), (4, 2, 1), (2, 2, 1), (6, 3, 1), (5, 3, 1)), 2).cache()
     val model = new IsotonicRegression().run(trainRDD)
 
     assert(model.predict(0) === 1)
@@ -181,7 +181,7 @@ class IsotonicRegressionSuite extends FunSuite with MLlibTestSparkContext with M
   test("antitonic regression prediction with duplicate features") {
     val trainRDD = sc.parallelize(
       Seq[(Double, Double, Double)](
-        (5, 1, 1), (6, 1, 1), (2, 2, 1), (4, 2, 1), (1, 3, 1), (2, 3, 1)), 2)
+        (5, 1, 1), (6, 1, 1), (2, 2, 1), (4, 2, 1), (1, 3, 1), (2, 3, 1)), 2).cache()
     val model = new IsotonicRegression().setIsotonic(false).run(trainRDD)
 
     assert(model.predict(0) === 6)
@@ -193,7 +193,7 @@ class IsotonicRegressionSuite extends FunSuite with MLlibTestSparkContext with M
   test("isotonic regression RDD prediction") {
     val model = runIsotonicRegression(Seq(1, 2, 7, 1, 2), true)
 
-    val testRDD = sc.parallelize(List(-2.0, -1.0, 0.5, 0.75, 1.0, 2.0, 9.0), 2)
+    val testRDD = sc.parallelize(List(-2.0, -1.0, 0.5, 0.75, 1.0, 2.0, 9.0), 2).cache()
     val predictions = testRDD.map(x => (x, model.predict(x))).collect().sortBy(_._1).map(_._2)
     assert(predictions === Array(1, 1, 1.5, 1.75, 2, 10.0/3, 10.0/3))
   }