SPARK-2748 [MLLIB] [GRAPHX] Loss of precision for small arguments to Math.exp, Math.log #1659
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QA tests have started for PR 1659. This patch merges cleanly. |
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QA results for PR 1659: |
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LGTM. Merged into master. Thanks!! |
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…Math.exp, Math.log 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 note the related PR for Python: apache#1652 Author: Sean Owen <[email protected]> Closes apache#1659 from srowen/SPARK-2748 and squashes the following commits: c5926d4 [Sean Owen] Use log1p, expm1 for better precision for tiny arguments
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In a few places in MLlib, an expression of the form
log(1.0 + p)is evaluated. When p is so small that1.0 + p == 1.0, the result is 0.0. However the correct answer is very nearp. This is whyMath.log1pexists.Similarly for one instance of
exp(m) - 1in GraphX; there's a specialMath.expm1method.While the errors occur only for very small arguments, given their use in machine learning algorithms, this is entirely possible.
Also note the related PR for Python: #1652