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https://issues.apache.org/jira/browse/FLINK-5588?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15866788#comment-15866788
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ASF GitHub Bot commented on FLINK-5588:
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GitHub user skonto opened a pull request:
https://github.com/apache/flink/pull/3313
[FLINK-5588][ml] add a data normalizer to ml library
- Adds a Normalizer.
- Still need to add the Unit Scaler for the features.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/skonto/flink unit_scaler
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/flink/pull/3313.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 #3313
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commit e25af5c8331214167277a982f0ec8de5b41a202d
Author: Stavos Kontopoulos <[email protected]>
Date: 2017-02-13T02:16:13Z
add the normalizer
commit f85ed43aadbc3bb3233d9c274dd352ad759cfec9
Author: Stavos Kontopoulos <[email protected]>
Date: 2017-02-14T21:55:46Z
add docs
----
> Add a unit scaler based on different norms
> ------------------------------------------
>
> Key: FLINK-5588
> URL: https://issues.apache.org/jira/browse/FLINK-5588
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Stavros Kontopoulos
> Assignee: Stavros Kontopoulos
> Priority: Minor
>
> So far ML has two scalers: min-max and the standard scaler.
> A third one frequently used, is the scaler to unit.
> We could implement a transformer for this type of scaling for different norms
> available to the user.
> I will make a separate class for the Normalization per sample procedure by
> using the Transformer API because it is easy to add
> it, fit method does nothing in this case.
> Scikit-learn has also some calls available outside the Transform API, we
> might want add that in the future.
> These calls work on any axis but they are not re-usable in a pipeline [4]
> Right now the existing scalers in Flink ML support per feature normalization
> by using the Transformer API.
> Resources
> [1] https://en.wikipedia.org/wiki/Feature_scaling
> [2]
> http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html
> [3] https://spark.apache.org/docs/2.1.0/mllib-feature-extraction.html
> [4] http://scikit-learn.org/stable/modules/preprocessing.html
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