From 589cce93c821ac28e9090a478f6e7465398b7c30 Mon Sep 17 00:00:00 2001 From: Gio Borje Date: Wed, 25 May 2016 16:52:31 -0500 Subject: Log warnings for numIterations * miniBatchFraction < 1.0 ## What changes were proposed in this pull request? Add a warning log for the case that `numIterations * miniBatchFraction <1.0` during gradient descent. If the product of those two numbers is less than `1.0`, then not all training examples will be used during optimization. To put this concretely, suppose that `numExamples = 100`, `miniBatchFraction = 0.2` and `numIterations = 3`. Then, 3 iterations will occur each sampling approximately 6 examples each. In the best case, each of the 6 examples are unique; hence 18/100 examples are used. This may be counter-intuitive to most users and led to the issue during the development of another Spark ML model: https://github.com/zhengruifeng/spark-libFM/issues/11. If a user actually does not require the training data set, it would be easier and more intuitive to use `RDD.sample`. ## How was this patch tested? `build/mvn -DskipTests clean package` build succeeds Author: Gio Borje Closes #13265 from Hydrotoast/master. --- .../scala/org/apache/spark/mllib/optimization/GradientDescent.scala | 5 +++++ 1 file changed, 5 insertions(+) (limited to 'mllib/src/main/scala') diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala index a67ea836e5..735e780909 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala @@ -197,6 +197,11 @@ object GradientDescent extends Logging { "< 1.0 can be unstable because of the stochasticity in sampling.") } + if (numIterations * miniBatchFraction < 1.0) { + logWarning("Not all examples will be used if numIterations * miniBatchFraction < 1.0: " + + s"numIterations=$numIterations and miniBatchFraction=$miniBatchFraction") + } + val stochasticLossHistory = new ArrayBuffer[Double](numIterations) // Record previous weight and current one to calculate solution vector difference -- cgit v1.2.3