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https://issues.apache.org/jira/browse/FLINK-5588?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Stavros Kontopoulos updated FLINK-5588:
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Description:
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
was:
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 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
> 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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