Github user rotationsymmetry commented on a diff in the pull request:
https://github.com/apache/spark/pull/8022#discussion_r38717322
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
---
@@ -194,4 +196,204 @@ class StreamingLinearRegressionSuite extends
SparkFunSuite with TestSuiteBase {
)
val output: Seq[Seq[(Double, Double)]] = runStreams(ssc, numBatches,
numBatches)
}
+
+ test("parameter accuracy with full memory (decayFactor = 1)") {
+ // create model
+ val model = new StreamingLinearRegressionWithSGD()
+ .setDecayFactor(1)
+ .setInitialWeights(Vectors.dense(0.0, 0.0))
+ .setStepSize(0.5)
+ .setNumIterations(50)
+ .setConvergenceTol(0.0001)
+
+ // generate sequence of simulated data
+ val numBatches = 10
+ // the first few RDD's are generated under the model A
+ val inputA = (0 until (numBatches-1)).map { i =>
+ LinearDataGenerator.generateLinearInput(0.0, Array(10.0, 10.0), 200,
42 * (i + 1))
+ }
+ // the last RDD is generated under the model B
+ val inputB =
+ LinearDataGenerator.generateLinearInput(0.0, Array(5.0, 3.0), 200,
42 * (numBatches + 1))
+ val input = inputA :+ inputB
+
+ // apply model training to input stream
+ ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => {
+ model.trainOn(inputDStream)
+ inputDStream.count()
+ })
+ runStreams(ssc, numBatches, numBatches)
+
+ // with full memory, the final parameter estimates should be close to
model A
+ assertEqual(model.latestModel().intercept, 0.0, 1.0)
+ assertEqual(model.latestModel().weights(0), 10.0, 1.0)
+ assertEqual(model.latestModel().weights(1), 10.0, 1.0)
+ }
+
+ test("parameter accuracy with no memory (decayFactor = 0)") {
+ // create model
+ val model = new StreamingLinearRegressionWithSGD()
+ .setDecayFactor(0)
+ .setInitialWeights(Vectors.dense(0.0, 0.0))
+ .setStepSize(0.5)
+ .setNumIterations(50)
+ .setConvergenceTol(0.0001)
+
+ // generate sequence of simulated data
+ val numBatches = 10
+ // the first few RDD's are generated under the model A
+ val inputA = (0 until (numBatches - 1)).map { i =>
+ LinearDataGenerator.generateLinearInput(0.0, Array(10.0, 10.0), 200,
42 * (i + 1))
+ }
+ // the last RDD is generated under the model B
+ val inputB =
+ LinearDataGenerator.generateLinearInput(0.0, Array(5.0, 3.0), 200,
42 * (numBatches + 1))
+ val input = inputA :+ inputB
+
+ // apply model training to input stream
+ ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => {
+ model.trainOn(inputDStream)
+ inputDStream.count()
+ })
+ runStreams(ssc, numBatches, numBatches)
+
+ // with no memory, the final parameter estimates should be close to
model B
+ assertEqual(model.latestModel().intercept, 0.0, 1.0)
--- End diff --
Sure.
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