Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1361#discussion_r15723317
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
---
@@ -0,0 +1,133 @@
+/*
+ * 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 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, MLUtils}
+import org.apache.spark.streaming.{Milliseconds, StreamingContext}
+import org.apache.spark.util.Utils
+
+class StreamingLinearRegressionSuite extends FunSuite with
LocalSparkContext {
+
+ // 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 testDir = Files.createTempDir()
+ val numBatches = 10
+ val batchDuration = Milliseconds(1000)
+ val ssc = new StreamingContext(sc, batchDuration)
+ val data = MLUtils.loadStreamingLabeledPoints(ssc, testDir.toString)
+ val model = new StreamingLinearRegressionWithSGD()
+ .setInitialWeights(Vectors.dense(0.0, 0.0))
+ .setStepSize(0.1)
+ .setNumIterations(50)
+
+ 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)
+ }
+
+ ssc.stop(stopSparkContext=false)
+
+ System.clearProperty("spark.driver.port")
+ Utils.deleteRecursively(testDir)
+
+ // check accuracy of final parameter estimates
+ assertEqual(model.latestModel().intercept, 0.0, 0.1)
+ assertEqual(model.latestModel().weights(0), 10.0, 0.1)
+ assertEqual(model.latestModel().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.latestModel().predict(row.features)), validationData)
--- End diff --
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