Streaming linear regression unit tests

- Test parameter estimate accuracy after several updates
- Test parameter accuracy improvement after each batch

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

@@ -0,0 +1,124 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.regression
+
+import java.io.File
+
+import com.google.common.io.Files
+import org.apache.commons.io.FileUtils
+import org.scalatest.FunSuite
+import org.apache.spark.SparkConf
+import org.apache.spark.streaming.{Milliseconds, Seconds, StreamingContext}
+import org.apache.spark.mllib.util.{MLStreamingUtils, LinearDataGenerator, LocalSparkContext}
+
+import scala.collection.mutable.ArrayBuffer
+
+class StreamingLinearRegressionSuite extends FunSuite {
+
+  // Assert that two values are equal within tolerance epsilon
+  def assertEqual(v1: Double, v2: Double, epsilon: Double) {
+    def errorMessage = v1.toString + " did not equal " + v2.toString
+    assert(math.abs(v1-v2) <= epsilon, errorMessage)
+  }
+
+  // Assert that model predictions are correct
+  def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) {
+    val numOffPredictions = predictions.zip(input).count { case (prediction, expected) =>
+      // A prediction is off if the prediction is more than 0.5 away from expected value.
+      math.abs(prediction - expected.label) > 0.5
+    }
+    // At least 80% of the predictions should be on.
+    assert(numOffPredictions < input.length / 5)
+  }
+
+  // Test if we can accurately learn Y = 10*X1 + 10*X2 on streaming data
+  test("streaming linear regression parameter accuracy") {
+
+    val conf = new SparkConf().setMaster("local").setAppName("streaming test")
+    val testDir = Files.createTempDir()
+    val numBatches = 10
+    val ssc = new StreamingContext(conf, Seconds(1))
+    val data = MLStreamingUtils.loadLabeledPointsFromText(ssc, testDir.toString)
+    val model = StreamingLinearRegressionWithSGD.start(numFeatures=2, numIterations=50)
+
+    model.trainOn(data)
+
+    ssc.start()
+
+    // write data to a file stream
+    Thread.sleep(5000)
+    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)
+      FileUtils.writeStringToFile(file, samples.map(x => x.toString).mkString("\n"))
+      Thread.sleep(Milliseconds(1000).milliseconds)
+    }
+    Thread.sleep(Milliseconds(5000).milliseconds)
+
+    ssc.stop()
+
+    System.clearProperty("spark.driver.port")
+    FileUtils.deleteDirectory(testDir)
+
+    // check accuracy of final parameter estimates
+    assertEqual(model.latest().intercept, 0.0, 0.1)
+    assertEqual(model.latest().weights(0), 10.0, 0.1)
+    assertEqual(model.latest().weights(1), 10.0, 0.1)
+
+    // check accuracy of predictions
+    val validationData = LinearDataGenerator.generateLinearInput(0.0, Array(10.0, 10.0), 100, 17)
+    validatePrediction(validationData.map(row => model.latest().predict(row.features)), validationData)
+  }
+
+  // Test that parameter estimates improve when learning Y = 10*X1 on streaming data
+  test("streaming linear regression parameter convergence") {
+
+    val conf = new SparkConf().setMaster("local").setAppName("streaming test")
+    val testDir = Files.createTempDir()
+    val ssc = new StreamingContext(conf, Seconds(1))
+    val numBatches = 5
+    val data = MLStreamingUtils.loadLabeledPointsFromText(ssc, testDir.toString)
+    val model = StreamingLinearRegressionWithSGD.start(numFeatures=1, numIterations=50)
+
+    model.trainOn(data)
+
+    ssc.start()
+
+    // write data to a file stream
+    val history = new ArrayBuffer[Double](numBatches)
+    Thread.sleep(5000)
+    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)
+      FileUtils.writeStringToFile(file, samples.map(x => x.toString).mkString("\n"))
+      Thread.sleep(Milliseconds(1000).milliseconds)
+      history.append(math.abs(model.latest().weights(0) - 10.0))
+    }
+    Thread.sleep(Milliseconds(5000).milliseconds)
+
+    ssc.stop()
+
+    System.clearProperty("spark.driver.port")
+    FileUtils.deleteDirectory(testDir)
+
+    // check that error is always getting smaller
+    assert(history.drop(1).zip(history.dropRight(1)).forall(x => (x._1 - x._2) < 0))
+
+  }
+
+}