Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/40#discussion_r10178907
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
---
@@ -104,4 +104,45 @@ class GradientDescentSuite extends FunSuite with
LocalSparkContext with ShouldMa
val lossDiff = loss.init.zip(loss.tail).map { case (lhs, rhs) => lhs -
rhs }
assert(lossDiff.count(_ > 0).toDouble / lossDiff.size > 0.8)
}
+
+ test("Test the loss and gradient of first iteration with
regularization.") {
+
+ val gradient = new LogisticGradient()
+ val updater = new SquaredL2Updater()
+
+ // Add a extra variable consisting of all 1.0's for the intercept.
+ val testData = GradientDescentSuite.generateGDInput(2.0, -1.5, 10000,
42)
+ val data = testData.map { case LabeledPoint(label, features) =>
+ label -> Array(1.0, features: _*)
+ }
+
+ val dataRDD = sc.parallelize(data, 2).cache()
+
+ // Prepare non-zero weights
+ val initialWeightsWithIntercept = Array(1.0, 0.5)
+
+ val regParam0 = 0
+ val (newWeights0, loss0) = GradientDescent.runMiniBatchSGD(
+ dataRDD, gradient, updater, 1, 1, regParam0, 1.0,
initialWeightsWithIntercept)
+
+ val regParam1 = 1
+ val (newWeights1, loss1) = GradientDescent.runMiniBatchSGD(
+ dataRDD, gradient, updater, 1, 1, regParam1, 1.0,
initialWeightsWithIntercept)
+
+ def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean =
{
+ math.abs(x - y) / (math.abs(y) + 1e-15) < tol
+ }
+
+ assert(compareDouble(
+ loss1(0),
+ loss0(0) + (math.pow(initialWeightsWithIntercept(0), 2) +
+ math.pow(initialWeightsWithIntercept(1), 2)) / 2),
+ """For non-zero weights, the regVal should be \frac{1}{2}\sum_i
w_i^2.""")
+
+ assert(
+ compareDouble(newWeights1(0) , newWeights0(0) -
initialWeightsWithIntercept(0)) &&
+ compareDouble(newWeights1(1) , newWeights0(1) -
initialWeightsWithIntercept(1)),
+ "The different between newWeights with/without regularization " +
+ "should be initialWeightsWithIntercept")
--- End diff --
Missing a period at the end.
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