SPARK-2748 [MLLIB] [GRAPHX] Loss of precision for small arguments to Math.exp, Math.log #1659
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
QA tests have started for PR 1659. This patch merges cleanly. |
|
QA results for PR 1659: |
|
LGTM. Merged into master. Thanks!! |
xiliu82
pushed a commit
to xiliu82/spark
that referenced
this pull request
Sep 4, 2014
…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 <srowen@gmail.com> Closes apache#1659 from srowen/SPARK-2748 and squashes the following commits: c5926d4 [Sean Owen] Use log1p, expm1 for better precision for tiny arguments
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
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