[
https://issues.apache.org/jira/browse/SPARK-2599?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14067876#comment-14067876
]
Sean Owen commented on SPARK-2599:
----------------------------------
The relative error will never be more than 2.0; it wouldn't be possible to use
epsilon >= 2.0 meaningfully -- think of expecting 0.005 but accepting anything
0.05 +/- 0.1. The range is also asymmetric, which might not be desired. With
expected value 2.0 and epsilon = 0.1, you allow the range 1.8 to 2.2222 instead
of 1.8 to 2.2.
The usual approach is to treat epsilon as an absolute error. This is fit for
purpose in its default role of ignoring variance due to rounding or machine
precision. The default can be near a ulp, and can be changed.
Is the intent really to make an *absolute* error bound whose default value is
some *relative proportion* of the absolute value of the arguments?
If relative error, then it should be relative to the absolute value of the
expected value, rather than just larger one, I think.
Of course, you would still be able to change the value of this relative
epsilon. But what do you do when the expected value is 0? you would have no way
of specifying an epsilon other than 0. And, working in relative terms might get
clunky; if the expected value is 0.123 +/ 0.45, the test writer has to work out
and document what the relative epsilon is doing.
Still, relative is useful. It seems like the right-est thing is two methods,
one specifying absolute and the other relative complexity. And that is a little
complex.
So I can see why every testing framework I've seen just lets you set an
absolute error and that's it. At the least, it's straightforward to reason
about and fully flexible. You do have to set the epsilon once in a while, but
that's always the case.
Net-net, I'm suggesting making this an absolute error only to address all this.
> almostEquals mllib.util.TestingUtils does not behave as expected when
> comparing against 0.0
> -------------------------------------------------------------------------------------------
>
> Key: SPARK-2599
> URL: https://issues.apache.org/jira/browse/SPARK-2599
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Reporter: Doris Xin
> Priority: Minor
>
> DoubleWithAlmostEquals.almostEquals, when used to compare a number with 0.0,
> would always produce an epsilon of 1 >> 1e-10, causing false failure when
> comparing very small numbers with 0.0.
--
This message was sent by Atlassian JIRA
(v6.2#6252)
