## 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 <gborje@linkedin.com>
Closes#13265 from Hydrotoast/master.
589cce93c8