[SPARK-3128][MLLIB] Use streaming test suite for StreamingLR

Refactored tests for streaming linear regression to use existing  streaming test utilities. Summary of changes:
- Made ``mllib`` depend on tests from ``streaming``
- Rewrote accuracy and convergence tests to use ``setupStreams`` and ``runStreams``
- Added new test for the accuracy of predictions generated by ``predictOnValue``

These tests should run faster, be easier to extend/maintain, and provide a reference for new tests.

mengxr tdas

Author: freeman <the.freeman.lab@gmail.com>

Closes #2037 from freeman-lab/streamingLR-predict-tests and squashes the following commits:

e851ca7 [freeman] Fixed long lines
50eb0bf [freeman] Refactored tests to use streaming test tools
32c43c2 [freeman] Added test for prediction

2 files changed, 74 insertions, 54 deletions

diff --git a/mllib/pom.xml b/mllib/pom.xml
index fc1ecfbea7..c7a1e2ae75 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -91,6 +91,13 @@
       <artifactId>junit-interface</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <type>test-jar</type>
+      <scope>test</scope>
+    </dependency>
   </dependencies>
   <profiles>
     <profile>
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
index 45e25eecf5..28489410f8 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
@@ -17,20 +17,19 @@
 
 package org.apache.spark.mllib.regression
 
-import java.io.File
-import java.nio.charset.Charset
-
 import scala.collection.mutable.ArrayBuffer
 
-import com.google.common.io.Files
 import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.util.{LinearDataGenerator, LocalSparkContext}
-import org.apache.spark.streaming.{Milliseconds, StreamingContext}
-import org.apache.spark.util.Utils
+import org.apache.spark.mllib.util.LinearDataGenerator
+import org.apache.spark.streaming.dstream.DStream
+import org.apache.spark.streaming.TestSuiteBase
+
+class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase {
 
-class StreamingLinearRegressionSuite extends FunSuite with LocalSparkContext {
+  // use longer wait time to ensure job completion
+  override def maxWaitTimeMillis = 20000
 
   // Assert that two values are equal within tolerance epsilon
   def assertEqual(v1: Double, v2: Double, epsilon: Double) {
@@ -49,35 +48,26 @@ 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
-    val batchDuration = Milliseconds(1000)
-    val ssc = new StreamingContext(sc, batchDuration)
-    val data = ssc.textFileStream(testDir.toString).map(LabeledPoint.parse)
+    // create model
     val model = new StreamingLinearRegressionWithSGD()
       .setInitialWeights(Vectors.dense(0.0, 0.0))
       .setStepSize(0.1)
-      .setNumIterations(50)
+      .setNumIterations(25)
 
-    model.trainOn(data)
-
-    ssc.start()
-
-    // write data to a file stream
-    for (i <- 0 until numBatches) {
-      val samples = LinearDataGenerator.generateLinearInput(
-        0.0, Array(10.0, 10.0), 100, 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)
+    // generate sequence of simulated data
+    val numBatches = 10
+    val input = (0 until numBatches).map { i =>
+      LinearDataGenerator.generateLinearInput(0.0, Array(10.0, 10.0), 100, 42 * (i + 1))
     }
 
-    ssc.stop(stopSparkContext=false)
-
-    System.clearProperty("spark.driver.port")
-    Utils.deleteRecursively(testDir)
+    // apply model training to input stream
+    val ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => {
+      model.trainOn(inputDStream)
+      inputDStream.count()
+    })
+    runStreams(ssc, numBatches, numBatches)
 
     // check accuracy of final parameter estimates
     assertEqual(model.latestModel().intercept, 0.0, 0.1)
@@ -91,39 +81,33 @@ 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)
-    val ssc = new StreamingContext(sc, batchDuration)
-    val numBatches = 5
-    val data = ssc.textFileStream(testDir.toString()).map(LabeledPoint.parse)
+    // create model
     val model = new StreamingLinearRegressionWithSGD()
       .setInitialWeights(Vectors.dense(0.0))
       .setStepSize(0.1)
-      .setNumIterations(50)
-
-    model.trainOn(data)
-
-    ssc.start()
+      .setNumIterations(25)
 
-    // write data to a file stream
-    val history = new ArrayBuffer[Double](numBatches)
-    for (i <- 0 until numBatches) {
-      val samples = LinearDataGenerator.generateLinearInput(0.0, Array(10.0), 100, 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)
-      // wait an extra few seconds to make sure the update finishes before new data arrive
-      Thread.sleep(4000)
-      history.append(math.abs(model.latestModel().weights(0) - 10.0))
+    // generate sequence of simulated data
+    val numBatches = 10
+    val input = (0 until numBatches).map { i =>
+      LinearDataGenerator.generateLinearInput(0.0, Array(10.0), 100, 42 * (i + 1))
     }
 
-    ssc.stop(stopSparkContext=false)
+    // create buffer to store intermediate fits
+    val history = new ArrayBuffer[Double](numBatches)
 
-    System.clearProperty("spark.driver.port")
-    Utils.deleteRecursively(testDir)
+    // apply model training to input stream, storing the intermediate results
+    // (we add a count to ensure the result is a DStream)
+    val ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => {
+      model.trainOn(inputDStream)
+      inputDStream.foreachRDD(x => history.append(math.abs(model.latestModel().weights(0) - 10.0)))
+      inputDStream.count()
+    })
+    runStreams(ssc, numBatches, numBatches)
 
+    // compute change in error
     val deltas = history.drop(1).zip(history.dropRight(1))
     // check error stability (it always either shrinks, or increases with small tol)
     assert(deltas.forall(x => (x._1 - x._2) <= 0.1))
@@ -132,4 +116,33 @@ class StreamingLinearRegressionSuite extends FunSuite with LocalSparkContext {
 
   }
 
+  // Test predictions on a stream
+  test("predictions") {
+
+    // create model initialized with true weights
+    val model = new StreamingLinearRegressionWithSGD()
+      .setInitialWeights(Vectors.dense(10.0, 10.0))
+      .setStepSize(0.1)
+      .setNumIterations(25)
+
+    // generate sequence of simulated data for testing
+    val numBatches = 10
+    val nPoints = 100
+    val testInput = (0 until numBatches).map { i =>
+      LinearDataGenerator.generateLinearInput(0.0, Array(10.0, 10.0), nPoints, 42 * (i + 1))
+    }
+
+    // apply model predictions to test stream
+    val ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => {
+      model.predictOnValues(inputDStream.map(x => (x.label, x.features)))
+    })
+    // collect the output as (true, estimated) tuples
+    val output: Seq[Seq[(Double, Double)]] = runStreams(ssc, numBatches, numBatches)
+
+    // compute the mean absolute error and check that it's always less than 0.1
+    val errors = output.map(batch => batch.map(p => math.abs(p._1 - p._2)).sum / nPoints)
+    assert(errors.forall(x => x <= 0.1))
+
+  }
+
 }