Use a different validation dataset for Logistic Regression prediction testing.

1 files changed, 17 insertions, 12 deletions

diff --git a/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala b/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala
index 6a8098b59d..0a99b78cf8 100644
--- a/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala
@@ -35,10 +35,11 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
 
   // Generate input of the form Y = logistic(offset + scale*X)
   def generateLogisticInput(
-    offset: Double,
-    scale: Double,
-    nPoints: Int) : Seq[(Double, Array[Double])]  = {
-    val rnd = new Random(42)
+      offset: Double,
+      scale: Double,
+      nPoints: Int,
+      seed: Int): Seq[(Double, Array[Double])]  = {
+    val rnd = new Random(seed)
     val x1 = Array.fill[Double](nPoints)(rnd.nextGaussian())
 
     // NOTE: if U is uniform[0, 1] then ln(u) - ln(1-u) is Logistic(0,1)
@@ -60,12 +61,12 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
   }
 
   def validatePrediction(predictions: Seq[Double], input: Seq[(Double, Array[Double])]) {
-    val offPredictions = predictions.zip(input).filter { case (prediction, (expected, _)) =>
+    val numOffPredictions = predictions.zip(input).filter { case (prediction, (expected, _)) =>
       // A prediction is off if the prediction is more than 0.5 away from expected value.
       math.abs(prediction - expected) > 0.5
     }.size
     // At least 80% of the predictions should be on.
-    assert(offPredictions < input.length / 5)
+    assert(numOffPredictions < input.length / 5)
   }
 
   // Test if we can correctly learn A, B where Y = logistic(A + B*X)
@@ -74,7 +75,7 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
     val A = 2.0
     val B = -1.5
 
-    val testData = generateLogisticInput(A, B, nPoints)
+    val testData = generateLogisticInput(A, B, nPoints, 42)
 
     val testRDD = sc.parallelize(testData, 2)
     testRDD.cache()
@@ -87,11 +88,13 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
     assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]")
     assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]")
 
+    val validationData = generateLogisticInput(A, B, nPoints, 17)
+    val validationRDD = sc.parallelize(validationData, 2)
     // Test prediction on RDD.
-    validatePrediction(model.predict(testRDD.map(_._2)).collect(), testData)
+    validatePrediction(model.predict(validationRDD.map(_._2)).collect(), validationData)
 
     // Test prediction on Array.
-    validatePrediction(testData.map(row => model.predict(row._2)), testData)
+    validatePrediction(validationData.map(row => model.predict(row._2)), validationData)
   }
 
   test("logistic regression with initial weights") {
@@ -99,7 +102,7 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
     val A = 2.0
     val B = -1.5
 
-    val testData = generateLogisticInput(A, B, nPoints)
+    val testData = generateLogisticInput(A, B, nPoints, 42)
 
     val initialB = -1.0
     val initialWeights = Array(initialB)
@@ -116,10 +119,12 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
     assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]")
     assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]")
 
+    val validationData = generateLogisticInput(A, B, nPoints, 17)
+    val validationRDD = sc.parallelize(validationData, 2)
     // Test prediction on RDD.
-    validatePrediction(model.predict(testRDD.map(_._2)).collect(), testData)
+    validatePrediction(model.predict(validationRDD.map(_._2)).collect(), validationData)
 
     // Test prediction on Array.
-    validatePrediction(testData.map(row => model.predict(row._2)), testData)
+    validatePrediction(validationData.map(row => model.predict(row._2)), validationData)
   }
 }