GitHub user Hydrotoast opened a pull request:
https://github.com/apache/spark/pull/13265
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
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/Hydrotoast/spark master
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/13265.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #13265
----
commit c7dd4c61b6d954504b6e03a58a4cdd47a5077883
Author: Gio Borje <[email protected]>
Date: 2016-05-23T20:45:52Z
Log warnings for numIterations * miniBatchFraction < 1.0 during gradient
descent
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