Github user thvasilo commented on a diff in the pull request:
https://github.com/apache/flink/pull/692#discussion_r30592697
--- Diff:
flink-staging/flink-ml/src/test/scala/org/apache/flink/ml/optimization/GradientDescentITSuite.scala
---
@@ -205,6 +190,61 @@ class GradientDescentITSuite extends FlatSpec with
Matchers with FlinkTestBase {
}
- // TODO: Need more corner cases
+ it should "terminate early if the convergence criterion is reached" in {
+ // TODO(tvas): We need a better way to check the convergence of the
weights.
+ // Ideally we want to have a Breeze-like system, where the optimizers
carry a history and that
+ // can tell us whether we have converged and at which iteration
+
+ val env = ExecutionEnvironment.getExecutionEnvironment
+
+ env.setParallelism(2)
+
+ val sgdEarlyTerminate = GradientDescent()
+ .setConvergenceThreshold(1e2)
+ .setStepsize(1.0)
+ .setIterations(800)
+ .setLossFunction(SquaredLoss())
+ .setRegularizationType(NoRegularization())
+ .setRegularizationParameter(0.0)
+
+ val inputDS = env.fromCollection(data)
+
+ val weightDSEarlyTerminate = sgdEarlyTerminate.optimize(inputDS, None)
+
+ val weightListEarly: Seq[WeightVector] =
weightDSEarlyTerminate.collect()
+
+ weightListEarly.size should equal(1)
+
+ val weightVectorEarly: WeightVector = weightListEarly.head
+ val weightsEarly =
weightVectorEarly.weights.asInstanceOf[DenseVector].data
+ val weight0Early = weightVectorEarly.intercept
+
+ val sgdNoConvergence = GradientDescent()
+ .setStepsize(1.0)
--- End diff --
Here we get a problem with the return type, we should be returning the
runtime type, if we call setLossFunction first we get back a Solver, which
means we can no longer call the IterativeSolver methods.
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