Minor Description
In a few places in MLlib, an expression of the form log(1.0 + p) is evaluated. When p is so small that 1.0 + p == 1.0, the result is 0.0. However the correct answer is very near p. This is why Math.log1p exists.
Similarly for one instance of exp(m) - 1 in GraphX; there's a special Math.expm1 method.
While the errors occur only for very small arguments, given their use in machine learning algorithms, this is entirely possible.
Also, while we're here, naftaliharris discovered a case in Python where 1 - 1 / (1 + exp(margin)) is less accurate than exp(margin) / (1 + exp(margin)). I don't think there's a JIRA on that one, so maybe this can serve as an umbrella for all of these related issues.
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