Added test for prediction

- Test predictOnValues for accuracy on a test stream

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

@@ -49,7 +49,7 @@ class StreamingLinearRegressionSuite extends FunSuite with LocalSparkContext {
   }
 
   // Test if we can accurately learn Y = 10*X1 + 10*X2 on streaming data
-  test("streaming linear regression parameter accuracy") {
+  test("parameter accuracy") {
 
     val testDir = Files.createTempDir()
     val numBatches = 10
@@ -76,7 +76,6 @@ class StreamingLinearRegressionSuite extends FunSuite with LocalSparkContext {
 
     ssc.stop(stopSparkContext=false)
 
-    System.clearProperty("spark.driver.port")
     Utils.deleteRecursively(testDir)
 
     // check accuracy of final parameter estimates
@@ -91,7 +90,7 @@ class StreamingLinearRegressionSuite extends FunSuite with LocalSparkContext {
   }
 
   // Test that parameter estimates improve when learning Y = 10*X1 on streaming data
-  test("streaming linear regression parameter convergence") {
+  test("parameter convergence") {
 
     val testDir = Files.createTempDir()
     val batchDuration = Milliseconds(2000)
@@ -121,7 +120,6 @@ class StreamingLinearRegressionSuite extends FunSuite with LocalSparkContext {
 
     ssc.stop(stopSparkContext=false)
 
-    System.clearProperty("spark.driver.port")
     Utils.deleteRecursively(testDir)
 
     val deltas = history.drop(1).zip(history.dropRight(1))
@@ -132,4 +130,67 @@ class StreamingLinearRegressionSuite extends FunSuite with LocalSparkContext {
 
   }
 
+  // Test predictions on a stream
+  test("predictions") {
+
+    val trainDir = Files.createTempDir()
+    val testDir = Files.createTempDir()
+    val batchDuration = Milliseconds(1000)
+    val numBatches = 10
+    val nPoints = 100
+
+    val ssc = new StreamingContext(sc, batchDuration)
+    val data = ssc.textFileStream(trainDir.toString).map(LabeledPoint.parse)
+    val model = new StreamingLinearRegressionWithSGD()
+      .setInitialWeights(Vectors.dense(0.0, 0.0))
+      .setStepSize(0.1)
+      .setNumIterations(50)
+
+    model.trainOn(data)
+
+    ssc.start()
+
+    // write training data to a file stream
+    for (i <- 0 until numBatches) {
+      val samples = LinearDataGenerator.generateLinearInput(
+        0.0, Array(10.0, 10.0), nPoints, 42 * (i + 1))
+      val file = new File(trainDir, i.toString)
+      Files.write(samples.map(x => x.toString).mkString("\n"), file, Charset.forName("UTF-8"))
+      Thread.sleep(batchDuration.milliseconds)
+    }
+
+    ssc.stop(stopSparkContext=false)
+
+    Utils.deleteRecursively(trainDir)
+
+    print(model.latestModel().weights.toArray.mkString(" "))
+    print(model.latestModel().intercept)
+
+    val ssc2 = new StreamingContext(sc, batchDuration)
+    val data2 = ssc2.textFileStream(testDir.toString).map(LabeledPoint.parse)
+
+    val history = new ArrayBuffer[Double](numBatches)
+    val predictions = model.predictOnValues(data2.map(x => (x.label, x.features)))
+    val errors = predictions.map(x => math.abs(x._1 - x._2))
+    errors.foreachRDD(rdd => history.append(rdd.reduce(_+_) / nPoints.toDouble))
+
+    ssc2.start()
+
+    // write test data to a file stream
+
+    // make a function
+    for (i <- 0 until numBatches) {
+      val samples = LinearDataGenerator.generateLinearInput(
+        0.0, Array(10.0, 10.0), nPoints, 42 * (i + 1))
+      val file = new File(testDir, i.toString)
+      Files.write(samples.map(x => x.toString).mkString("\n"), file, Charset.forName("UTF-8"))
+      Thread.sleep(batchDuration.milliseconds)
+    }
+
+    println(history)
+
+    ssc2.stop(stopSparkContext=false)
+
+  }
+
 }