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https://issues.apache.org/jira/browse/MADLIB-1168?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16389158#comment-16389158
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ASF GitHub Bot commented on MADLIB-1168:
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GitHub user iyerr3 opened a pull request:
https://github.com/apache/madlib/pull/239
Balance Sample: Add support for grouping
JIRA: MADLIB-1168
This commit adds grouping support for balanced sampling.
Grouping is implemented as a loop over the existing logic,
with the sampling for each group run independently.
Closes #239
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/madlib/madlib
feature/balanced-datasets-grouping
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/madlib/pull/239.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #239
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commit 6c5fcfb375eaf7dc68e1ede4aca2a47b8e55309b
Author: Rahul Iyer <riyer@...>
Date: 2018-02-24T02:45:32Z
Clean code + conform to PEP8
commit a5a0c1e2c851a923b9eb550d42dfc594b4635c64
Author: Rahul Iyer <riyer@...>
Date: 2018-02-26T23:01:34Z
Add a Collate plpy results function
commit 8e8eca2960207ca0317ded68608c660b8d4ddb55
Author: Rahul Iyer <riyer@...>
Date: 2018-03-02T00:44:54Z
Add grouping in get_level_frequency_distribution
commit cad4a5be732f89504ff62f4d9e68367d174fc322
Author: Rahul Iyer <riyer@...>
Date: 2018-03-07T07:07:00Z
Ensure subqueries are filtering groups and using right count
commit 39dd6f436bb9b8d505be5204226dcc3053b1b4df
Author: Rahul Iyer <riyer@...>
Date: 2018-03-07T07:07:14Z
Update install check to include grouping
commit d61ff28290dad27ead0f1c68d740a8ccb79f4aec
Author: Rahul Iyer <riyer@...>
Date: 2018-03-07T07:07:27Z
Update documentation with grouping examples
----
> Balance datasets
> ----------------
>
> Key: MADLIB-1168
> URL: https://issues.apache.org/jira/browse/MADLIB-1168
> Project: Apache MADlib
> Issue Type: New Feature
> Components: Module: Sampling
> Reporter: Frank McQuillan
> Assignee: ssoni
> Priority: Major
> Fix For: v1.14
>
> Attachments: MADlib Balance Datasets Requirements.pdf,
> MADlib_Balance_Datasets_Requirements_v2.pdf
>
>
> From [1] here is the motivation behind balancing datasets:
> “Most classification algorithms will only perform optimally when the number
> of samples of each class is roughly the same. Highly skewed datasets, where
> the minority is heavily outnumbered by one or more classes, have proven to be
> a challenge while at the same time becoming more and more common.
> One way of addressing this issue is by re-sampling the dataset as to offset
> this imbalance with the hope of arriving at a more robust and fair decision
> boundary than you would otherwise.
> Re-sampling techniques can be divided in these categories:
> * Under-sampling the majority class(es).
> * Over-sampling the minority class.
> * Combining over- and under-sampling.
> * Create ensemble balanced sets.”
> There is an extensive literature on balancing datasets. The plan for MADlib
> in the initial phase is to offer basic functionality that can be extended in
> later phases based on feedback from users.
> Please see attached document for proposed scope of this story.
> References
> [1] imbalance-learn Python project
> http://contrib.scikit-learn.org/imbalanced-learn/stable/index.html
> https://github.com/scikit-learn-contrib/imbalanced-learn
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